Systems and methods for image reconstruction in positron emission tomography

ABSTRACT

A system for PET image reconstruction is provided. The system may obtain PET data of a subject. The PET data may be associated with a plurality of coincidence events, which includes scattering events. The system may also generate a preliminary scatter sinogram relating to the scattering events based on the PET data. The system may also generate a target scatter sinogram relating to the scattering events by applying a scatter sinogram generator based on the preliminary scatter sinogram. The target scatter sinogram may have a higher image quality than the preliminary scatter sinogram. The system may further reconstruct a target PET image of the subject based on the PET data and the target scatter sinogram.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to Chinese Patent Application No.201910352350.7, filed on Apr. 28, 2019, the contents of which are herebyincorporated by reference.

TECHNICAL FIELD

The present disclosure generally relates to image reconstruction, andmore particularly, relates to systems and methods for PET imagereconstruction.

BACKGROUND

Recently, PET has been widely used in clinical examination and diseasediagnosis. For example, a subject may be injected with a radioactivetracer before a PET scan, and PET data of the subject may be obtainedduring the PET scan. The PET data may relate to a plurality ofcoincidence events, including true coincidence events, scatteringevents, and random events, detected in the PET scan. However, only thetrue coincidence events may accurately indicate position information ofthe radioactive tracer injected into the subject. In PET imagereconstruction, data correction may be needed to reduce or eliminate theeffect of the scattering events and/or the random events. For example,techniques, such as a Monte-Carlo Simulation algorithm, an analyticalmodeling technique, a source modulation algorithm, have been used togenerate a scatter sinogram for scatter correction in the PET imagereconstruction. Therefore, it is desirable to provide systems andmethods for PET image reconstruction, thereby improving thereconstruction accuracy and/or efficiency.

SUMMARY

According to one aspect of the present disclosure, a system for PETimage reconstruction is provided. The system may include at least onestorage device may include a set of instructions and at least oneprocessor configured to communicate with the at least one storagedevice. When executing the set of instructions, the at least oneprocessor may be configured to direct the system to obtain PET data of asubject. The PET data may be associated with a plurality of coincidenceevents including scattering events. The at least one processor may alsobe configured to direct the system to generate a preliminary scattersinogram relating to the scattering events based on the PET data, andgenerate a target scatter sinogram relating to the scattering events byapplying a scatter sinogram generator based on the preliminary scattersinogram. The target scatter sinogram may have a higher image qualitythan the preliminary scatter sinogram. The at least one processor mayfurther be configured to direct the system to reconstruct a target PETimage of the subject based on the PET data and the target scattersinogram.

In some embodiments, the at least one processor may be configured todirect the system to generate a preliminary PET image of the subjectbased on the PET data, and generate the preliminary scatter sinogrambased on the preliminary PET image. The target PET image may includeless scattering noises than the preliminary PET image.

In some embodiments, the at least one processor may be configured todirect the system to obtain an attenuation map of the subject, andgenerate the preliminary scatter sinogram based on the attenuation mapand the preliminary PET image.

In some embodiments, the preliminary scatter sinogram may be generatedbased on the attenuation map and the preliminary PET image according toa Monte-Carlo simulation algorithm.

In some embodiments, the plurality of coincidence events may furtherinclude true coincidence events. The at least one processor may beconfigured to direct the system to generate a prompt sinogram and adelay sinogram based on PET data, and determine a true sinogram relatingthe true coincidence events and the scattering events based on theprompt sinogram and the delay sinogram. The at least one processor mayfurther be configured to direct the system to generate the preliminaryPET image of the subject based on the true sinogram.

In some embodiments, the at least one processor may be configured todirect the system to generate the target scatter sinogram by processingthe preliminary scatter sinogram and the true sinogram using the scattersinogram generator.

In some embodiments, the at least one processor may be configured todirect the system to normalize the preliminary scatter sinogram and thetrue sinogram, and generate a concatenated sinogram by concatenating thenormalized preliminary scatter sinogram and the normalized truesinogram. The at least one processor may further be configured to directthe system to generate the target scatter sinogram by processing theconcatenated sinogram using the scatter sinogram generator.

In some embodiments, the at least one processor may be configured todirect the system to generate an updated PET image of the subject basedon the target scatter sinogram and the PET data, and generate a finalscatter correction sinogram based on the target scatter sinogram and theupdated PET image. The at least one processor may further be configuredto direct the system to reconstruct the target PET image of the subjectbased on the PET data and the final scatter correction sinogram.

In some embodiments, the at least one processor may be configured todirect the system to generate a scatter correction sinogram bycorrecting the target scatter sinogram, and generate an updated PETimage of the subject based on the scatter correction sinogram and thePET data. The generating a final scatter correction sinogram based onthe target scatter sinogram and the updated PET image may includegenerating the final scatter correction sinogram by iteratively updatingthe scatter correction sinogram based on the updated PET image.

In some embodiments, the scatter sinogram generator may include aplurality of sequentially connected layers. The plurality ofsequentially connected layers may include a plurality of residuallayers. At least one of the plurality of residual layers may include afirst residual block and a second residual block. The first residualblock may be connected to a next layer via a downsampling path, and thesecond residual block may be connected to a previous layer via anupsampling path.

In some embodiments, the scatter sinogram generator may be trainedaccording to a model training process. The training process may includeobtaining a plurality of training samples, and generating the scattersinogram generator by training a preliminary model using the pluralityof training samples. Each of the training samples may include a samplepreliminary scatter sinogram and a sample target scatter sinogramrelating to sample scattering events detected in a sample PET scan of asample subject. The sample target scatter sinogram may have a higherimage quality than the sample preliminary scatter sinogram.

In some embodiments, the image quality of a certain scatter sinogram mayrelate to at least one of a noise level, a contrast ratio, or asmoothness of the certain scatter sinogram. The certain scatter sinogrammay be the target scatter sinogram or the preliminary scatter sinogram.

According to another aspect of the present disclosure, a system isprovided. The system may include at least one storage device storing aset of instructions for generating a scatter sinogram generator and atleast one processor configured to communicate with the at least onestorage device. When executing the set of instructions, the at least oneprocessor may be configured to direct the system to obtain a pluralityof training samples, and generate the scatter sinogram generator bytraining a preliminary model using the plurality of training samples.Each of training samples may include a sample preliminary scattersinogram and a sample target scatter sinogram relating to samplescattering events detected in a sample PET scan of a sample subject. Thesample target scatter sinogram may have a higher quality than the samplepreliminary scatter sinogram.

In some embodiments, each of the plurality of training samples mayinclude a sample true sinogram relating to sample true coincidenceevents and the sample scattering events detected in the correspondingsample PET scan.

In some embodiments, for each of the plurality of training samples, theat least one processor may be configured to direct the system tonormalize the sample preliminary scatter sinogram, the sample targetscatter sinogram, and the sample true sinogram of the training sample,and generate a cropped sample preliminary scatter sinogram, a croppedsample target scatter sinogram, a cropped sample true sinogram bycropping the normalized sample preliminary scatter sinogram, thenormalized sample target scatter sinogram, and the normalized sampletrue sinogram of the training sample, respectively. For each of theplurality of training samples, the at least one processor may beconfigured to direct the system to generate a sample concatenatedsinogram by concatenating the cropped sample preliminary scattersinogram and the cropped sample true sinogram. For each of the pluralityof training samples, the at least one processor may be configured todirect the system to generate the scatter sinogram generator by trainingthe preliminary model using the sample concatenated sinogram and thecropped sample target scatter sinogram of each of the plurality oftraining samples.

In some embodiments, the at least one processor may be configured todirect the system to generate a preliminary scatter sinogram generatorby training the preliminary model using the plurality of trainingsamples according to a first gradient descent algorithm. The at leastone processor may be configured to direct the system to generate thescatter sinogram generator by training the preliminary scatter sinogramgenerator using the plurality of training samples according to a secondgradient descent algorithm. The second gradient descent algorithm may bedifferent from the first gradient descent algorithm.

In some embodiments, the first gradient descent algorithm may be an Adamoptimization algorithm, and the second gradient descent algorithm may bea stochastic gradient descent (SGD)+Momentum optimization algorithm.

In some embodiments, the at least one processor may be configured todirect the system to determine a first learning rate with respect to thefirst gradient descent algorithm according to a learning rate range testtechnique, and determine a second learning rate with respect to thesecond gradient descent algorithm according to a cycle learning ratetechnique. The generating a preliminary scatter sinogram generator bytraining the preliminary model using the plurality of training samplesaccording to a first gradient descent algorithm may include generatingthe preliminary scatter sinogram generator by training the preliminarymodel using the plurality of training samples according to the firstgradient descent algorithm and the first learning rate. The generatingthe scatter sinogram generator by training the preliminary scattersinogram generator using the plurality of training samples according toa second gradient descent algorithm may include generating the scattersinogram generator by training the preliminary scatter sinogramgenerator using the plurality of training samples according to thesecond gradient descent algorithm and the second learning rate.

In some embodiments, the scatter sinogram generator may include aplurality of sequentially connected layers. The plurality ofsequentially connected layers may include a plurality of residuallayers. At least one of the plurality of residual layers may include afirst residual block and a second residual block. The first residualblock may be connected to a next layer via a downsampling path, and thesecond residual block may be connected to a previous layer via anupsampling path.

According to another aspect of the present disclosure, a method forpositron emission tomography (PET) image reconstruction is provided. Themethod may include obtaining PET data of a subject, the PET data beingassociated with a plurality of coincidence events. The plurality ofcoincidence events may include scattering events. The method may alsoinclude generating a preliminary scatter sinogram relating to thescattering events based on the PET data, and generating a target scattersinogram relating to the scattering events by applying a scattersinogram generator based on the preliminary scatter sinogram. The targetscatter sinogram may have a higher image quality than the preliminaryscatter sinogram. The method may include reconstructing a target PETimage of the subject based on the PET data and the target scattersinogram.

According to another aspect of the present disclosure, a method forgenerating a scatter sinogram generator is provided. The method mayinclude obtaining a plurality of training samples, and generating thescatter sinogram generator by training a preliminary model using theplurality of training samples. Each of the training samples may includea sample preliminary scatter sinogram and a sample target scattersinogram relating to sample scattering events detected in a sample PETscan of a sample subject. The sample target scatter sinogram may have ahigher quality than the sample preliminary scatter sinogram.

According to another aspect of the present disclosure, a non-transitorycomputer-readable storage medium including instructions is provided.When accessed by at least one processor of a system for positronemission tomography (PET) image reconstruction, the instructions causethe system to perform a method. The method may include obtaining PETdata of a subject, the PET data being associated with a plurality ofcoincidence events. The plurality of coincidence events may includescattering events. The method may also include generating a preliminaryscatter sinogram relating to the scattering events based on the PETdata, and generating a target scatter sinogram relating to thescattering events by applying a scatter sinogram generator based on thepreliminary scatter sinogram. The target scatter sinogram may have ahigher image quality than the preliminary scatter sinogram. The methodmay include reconstructing a target PET image of the subject based onthe PET data and the target scatter sinogram.

According to still another aspect of the present disclosure, anon-transitory computer-readable storage medium including instructionsis provided. When accessed by at least one processor of a system forgenerating a scatter sinogram generator, the instructions cause thesystem to perform a method. The method may include obtaining a pluralityof training samples, and generating the scatter sinogram generator bytraining a preliminary model using the plurality of training samples.Each of the training samples may include a sample preliminary scattersinogram and a sample target scatter sinogram relating to samplescattering events detected in a sample PET scan of a sample subject. Thesample target scatter sinogram may have a higher quality than the samplepreliminary scatter sinogram.

Additional features will be set forth in part in the description whichfollows, and in part will become apparent to those skilled in the artupon examination of the following and the accompanying drawings or maybe learned by production or operation of the examples. The features ofthe present disclosure may be realized and attained by practice or useof various aspects of the methodologies, instrumentalities, andcombinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. The drawings are not to scale. Theseembodiments are non-limiting exemplary embodiments, in which likereference numerals represent similar structures throughout the severalviews of the drawings, and wherein:

FIG. 1 is a schematic diagram illustrating an exemplary imaging systemaccording to some embodiments of the present disclosure;

FIG. 2 is a schematic diagram illustrating exemplary hardware and/orsoftware components of a computing device according to some embodimentsof the present disclosure;

FIG. 3 is a schematic diagram illustrating exemplary hardware and/orsoftware components of a mobile device according to some embodiments ofthe present disclosure;

FIGS. 4A and 4B are block diagrams illustrating exemplary processingdevices according to some embodiments of the present disclosure;

FIG. 5 is a flowchart illustrating an exemplary process forreconstructing a target PET image of a subject according to someembodiments of the present disclosure;

FIG. 6 is a flowchart illustrating an exemplary process for generating apreliminary scatter sinogram according to some embodiments of thepresent disclosure;

FIG. 7 is a flowchart illustrating an exemplary process forreconstructing a target PET image according to some embodiments of thepresent disclosure;

FIG. 8 is a flowchart illustrating an exemplary process for generating ascatter sinogram generator according to some embodiments of the presentdisclosure;

FIG. 9 is a schematic diagram illustrating an exemplary training stageand prediction stage of a scatter sinogram generator according to someembodiments of the present disclosure;

FIG. 10 is a schematic diagram illustrating an exemplary preliminarymodel according to some embodiments of the present disclosure;

FIG. 11 is a schematic diagram illustrating exemplary sinograms of afirst subject according to some embodiments of the present disclosure;and

FIG. 12 is a schematic diagram illustrating exemplary sinograms of asecond subject according to some embodiments of the present disclosure.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant disclosure. However, it should be apparent to those skilledin the art that the present disclosure may be practiced without suchdetails. In other instances, well-known methods, procedures, systems,components, and/or circuitry have been described at a relativelyhigh-level, without detail, in order to avoid unnecessarily obscuringaspects of the present disclosure. Various modifications to thedisclosed embodiments will be readily apparent to those skilled in theart, and the general principles defined herein may be applied to otherembodiments and applications without departing from the spirit and scopeof the present disclosure. Thus, the present disclosure is not limitedto the embodiments shown, but to be accorded the widest scope consistentwith the claims.

The terminology used herein is for the purpose of describing particularexample embodiments only and is not intended to be limiting. As usedherein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprise,”“comprises,” and/or “comprising,” “include,” “includes,” and/or“including,” when used in this specification, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof.

It will be understood that the term “system,” “engine,” “unit,”“module,” and/or “block” used herein are one method to distinguishdifferent components, elements, parts, sections or assembly of differentlevels in ascending order. However, the terms may be displaced byanother expression if they achieve the same purpose.

Generally, the word “module,” “unit,” or “block,” as used herein, refersto logic embodied in hardware or firmware, or to a collection ofsoftware instructions. A module, a unit, or a block described herein maybe implemented as software and/or hardware and may be stored in any typeof non-transitory computer-readable medium or another storage device. Insome embodiments, a software module/unit/block may be compiled andlinked into an executable program. It will be appreciated that softwaremodules can be callable from other modules/units/blocks or fromthemselves, and/or may be invoked in response to detected events orinterrupts. Software modules/units/blocks configured for execution oncomputing devices (e.g., processor 210 as illustrated in FIG. 2) may beprovided on a computer-readable medium, such as a compact disc, adigital video disc, a flash drive, a magnetic disc, or any othertangible medium, or as a digital download (and can be originally storedin a compressed or installable format that needs installation,decompression, or decryption prior to execution). Such software code maybe stored, partially or fully, on a storage device of the executingcomputing device, for execution by the computing device. Softwareinstructions may be embedded in firmware, such as an EPROM. It will befurther appreciated that hardware modules/units/blocks may be includedin connected logic components, such as gates and flip-flops, and/or canbe included of programmable units, such as programmable gate arrays orprocessors. The modules/units/blocks or computing device functionalitydescribed herein may be implemented as software modules/units/blocks,but may be represented in hardware or firmware. In general, themodules/units/blocks described herein refer to logicalmodules/units/blocks that may be combined with othermodules/units/blocks or divided into sub-modules/sub-units/sub-blocksdespite their physical organization or storage. The description may beapplicable to a system, an engine, or a portion thereof.

It will be understood that when a unit, engine, module or block isreferred to as being “on,” “connected to,” or “coupled to,” anotherunit, engine, module, or block, it may be directly on, connected orcoupled to, or communicate with the other unit, engine, module, orblock, or an intervening unit, engine, module, or block may be present,unless the context clearly indicates otherwise. As used herein, the term“and/or” includes any and all combinations of one or more of theassociated listed items. The term “image” in the present disclosure isused to collectively refer to image data (e.g., scan data, projectiondata) and/or images of various forms, including a two-dimensional (2D)image, a three-dimensional (3D) image, a four-dimensional (4D), etc. Theterm “pixel” and “voxel” in the present disclosure are usedinterchangeably to refer to an element of an image.

These and other features, and characteristics of the present disclosure,as well as the methods of operation and functions of the relatedelements of structure and the combination of parts and economies ofmanufacture, may become more apparent upon consideration of thefollowing description with reference to the accompanying drawings, allof which form a part of this disclosure. It is to be expresslyunderstood, however, that the drawings are for the purpose ofillustration and description only and are not intended to limit thescope of the present disclosure. It is understood that the drawings arenot to scale.

Provided herein are systems and methods for non-invasive biomedicalimaging, such as for disease diagnostic or research purposes. In someembodiments, the systems may include a single modality imaging systemand/or a multi-modality imaging system. The single modality imagingsystem may include, for example, a positron emission tomography (PET)system. The multi-modality imaging system may include, for example, apositron emission tomography-computed tomography (PET-CT) system, apositron emission tomography-magnetic resonance imaging (PET-MRI)system. It should be noted that the imaging system described below ismerely provided for illustration purposes, and not intended to limit thescope of the present disclosure.

The term “imaging modality” or “modality” as used herein broadly refersto an imaging method or technology that gathers, generates, processes,and/or analyzes imaging information of a subject. The subject mayinclude a biological object and/or a non-biological object. Thebiological object may be a human being, an animal, a plant, or a portionthereof (e.g., a cell, a tissue, an organ, etc.). In some embodiments,the subject may be a man-made composition of organic and/or inorganicmatters that are with or without life.

An aspect of the present disclosure relates to systems and methods forPET image reconstruction. The systems and methods may obtain PET data ofa subject. The PET data may be associated with a plurality ofcoincidence events, including scattering events, detected in a PET scanof the subject. The systems and methods may generate, based on the PETdata, a preliminary scatter sinogram relating to the scattering events.The systems and methods may also generate, based on the preliminaryscatter sinogram, a target scatter sinogram relating to the scatteringevents using a scatter sinogram generator. The target scatter sinogrammay have a higher image quality (e.g., a lower noise level) than thepreliminary scatter sinogram. The systems and methods may furtherreconstruct a target PET image of the subject based on the PET data andthe target scatter sinogram.

According to some embodiments of the present disclosure, the systems andmethods may first generate the preliminary scatter sinogram of arelatively low image quality (e.g., a high noise level) using a smallportion of the coincidence events of the PET data, and then generate,using the scatter sinogram generator, the target scatter sinogram with ahigher image quality (e.g., a reduced noise level) than the preliminaryscatter sinogram. For example, the preliminary scatter sinogram may begenerated according to a Monte-Carlo Simulation algorithm, which maysimulate the interaction between the subject and photons and generatethe preliminary scatter sinogram by building a statistical model.Normally, the Monte-Carlo Simulation algorithm may simulate a scattersinogram having a desired image quality (e.g., a low noise level) basedon a large amount of coincidence events. For example, if otherconditions remain the same, the more coincidence events are used in theMonte-Carlo Simulation, the higher accuracy the resulting scattersinogram may have. However, using more coincidence events in theMonte-Carlo Simulation may cost more computational resources (e.g., astorage space, system load indicating the amount of computational workinvolved), call for a larger number (or count) of computer devicesand/or more advanced computing devices, and/or cause a longer scanningor image reconstruction time in clinical examinations and/or treatment.The systems and methods as disclosed herein may be used to generate thetarget scatter sinogram with an improved reconstruction efficiency bytaking advantage of a reduced computational complexity and/or resourcesneeded for generating the preliminary scatter sinogram. For example, thegeneration of a scatter sinogram having a similar image quality to thetarget scatter sinogram may cost less than 1 second using the systemsand methods disclosed herein, compared to using conventional simulationalgorithms which may take more time (e.g., 4 seconds, 10 seconds).

Another aspect of the present disclosure relates to systems and methodsfor generating a scatter sinogram generator. The systems and methods mayobtain a plurality of training samples each of which includes a samplepreliminary scatter sinogram and a sample target scatter sinogramrelating to sample scattering events detected in a sample PET scan of asample subject, wherein the sample target scatter sinogram may have ahigher image quality than the sample preliminary scatter sinogram. Thesystems and methods may further generate the scatter sinogram generatorby training a preliminary model using the plurality of training samples.

In some embodiments, the training of the preliminary model may includetwo or more training stages in which different gradient descentalgorithms and/or different learning rate strategies are adopted, whichmay facilitate the convergence of the preliminary model and improve theaccuracy and/or the generalization ability of the resulting scattersinogram generator. In addition, a specially designed modelconfiguration is provided in the present disclosure. For example, thepreliminary model may be a U-shaped model including a plurality ofresidual blocks. Compared with a conventional U-net model that mainlyuses convolution blocks for feature extraction, the preliminary modeldisclosed herein may achieve an improved training accuracy andefficiency because the residual blocks may better preserve informationor features, avoid the problem of vanishing gradient, and facilitatemodel convergence in model training.

FIG. 1 is a schematic diagram illustrating an exemplary imaging system100 according to some embodiments of the present disclosure. As shown,the imaging system 100 may include a scanner 110, a network 120, one ormore terminals 130, a processing device 140, and a storage device 150.In some embodiments, the scanner 110, the terminal(s) 130, theprocessing device 140, and/or the storage device 150 may be connected toand/or communicate with each other via a wireless connection (e.g., thenetwork 120), a wired connection, or a combination thereof. Theconnection between the components of the imaging system 100 may bevariable. Merely by way of example, the scanner 110 may be connected tothe processing device 140 through the network 120, as illustrated inFIG. 1. As another example, the scanner 110 may be connected to theprocessing device 140 directly. As a further example, the storage device150 may be connected to the processing device 140 through the network120, as illustrated in FIG. 1, or connected to the processing device 140directly. As still a further example, a terminal 130 may be connected tothe processing device 140 through the network 120, as illustrated inFIG. 1, or connected to the processing device 140 directly.

The scanner 110 may generate or provide image data related to a subjectvia scanning the subject. In some embodiments, the subject may include abiological object and/or a non-biological object. For example, thesubject may include a specific portion of a body, such as a head, athorax, an abdomen, or the like, or a combination thereof. In someembodiments, the scanner 110 may include a single-modality scanner(e.g., a CT scanner, a PET scanner) and/or multi-modality scanner (e.g.,a PET-CT scanner, a PET-MRI scanner) as described elsewhere in thisdisclosure.

In some embodiments, the scanner 110 may include a gantry 111, adetector 112, a detecting region 113, and a scanning table 114. Thegantry 111 may support the detector 112. The detector 112 may detectradiation events (e.g., gamma photons) emitted from the detection region113. In some embodiments, the detector 112 may include one or moredetector units. The detector units may be assembled in any suitablemanner, for example, a ring, an arc, a rectangle, an array, or the like,or any combination thereof. In some embodiments, a detector unit mayinclude one or more crystal elements (e.g., scintillators) and/or one ormore photomultipliers (e.g., silicon photomultiplier (SiPM),photomultiplier tube (PMT)). The scanning table 114 may transport thesubject into and out of, and facilitate the positioning of the subjectin the detection region 113. In some embodiments, the detected radiationevents may be stored or archived in a storage device (e.g., the storagedevice 150), displayed on a display, or transferred to an externalstorage device via a cable, or a wired or wireless network (e.g., thenetwork 120). In some embodiments, a user may control the scanner 110via the processing device 140 and/or the terminal(s) 130.

In some embodiments, the scanner 110 may be a PET scanner. Beforescanning, a radioactive tracer isotope may be injected into the subjectto be scanned. One or more atoms of the tracer isotope may be chemicallyincorporated into biologically active molecules in the subject. Theactive molecules may become concentrated in a tissue of interest withinthe subject. The tracer isotope may undergo positron emission decay andemit positrons. A positron may travel a short distance (e.g., about 1mm) within a tissue of interest, lose kinetic energy, and interact withan electron of the subject. The positron and the electron may annihilateand produce a pair of annihilation photons. The pair of annihilationphotons (or radiation rays) may move in approximately oppositedirections. A plurality of radiation rays may reach the detector 112 andbe detected by the detector 112.

In some embodiments, one or more coincidence events may be determinedbased on the interaction positions and the interaction times of aplurality of received photons. If two photons are received and interactwith two scintillators of two detector units within a certaincoincidence time window (e.g., 1 nanosecond, 2 nanoseconds, 5nanoseconds, 10 nanoseconds, 20 nanoseconds, etc.), the two photons maybe deemed to come from the same annihilation, and regarded as acoincidence event (or coincident event). The coincidence event may beassigned to a line of response (LOR) joining the two relevant detectorunits that have detected the coincidence event.

A coincident event may be a true coincident event, a random event, ascattering event, etc. A true coincident event occurs when two photonsfrom a single annihilation event are detected by a pair of detectorunits along an LOR within a certain coincidence time window. A randomevent occurs when two photons from two separate annihilation eventsdetected by a pair of detector units are deemed as a coincident eventalong an LOR within the certain coincidence time window. A scatteringevent occurs when at least one of two photons detected by a pair ofdetector units along an LOR within the certain coincidence time windowhas undergone a Compton scattering prior to its detection.

The network 120 may include any suitable network that can facilitate theexchange of information and/or data for the imaging system 100. In someembodiments, one or more components of the imaging system 100 (e.g., thescanner 110, the processing device 140, the storage device 150, theterminal(s) 130) may communicate information and/or data with one ormore other components of the imaging system 100 via the network 120. Forexample, the processing device 140 may obtain PET data of a subject fromthe scanner 110 via the network 120. As another example, the processingdevice 140 may obtain user instruction(s) from the terminal(s) 130 viathe network 120. The network 120 may be or include a public network(e.g., the Internet), a private network (e.g., a local area network(LAN)), a wired network, a wireless network (e.g., an 802.11 network, aWi-Fi network), a frame relay network, a virtual private network (VPN),a satellite network, a telephone network, routers, hubs, switches,server computers, and/or any combination thereof. For example, thenetwork 120 may include a cable network, a wireline network, afiber-optic network, a telecommunications network, an intranet, awireless local area network (WLAN), a metropolitan area network (MAN), apublic telephone switched network (PSTN), a Bluetooth™ network, aZigBee™ network, a near field communication (NFC) network, or the like,or any combination thereof. In some embodiments, the network 120 mayinclude one or more network access points. For example, the network 120may include wired and/or wireless network access points such as basestations and/or internet exchange points through which one or morecomponents of the imaging system 100 may be connected to the network 120to exchange data and/or information.

The terminal(s) 130 may be connected to and/or communicate with thescanner 110, the processing device 140, and/or the storage device 150.For example, the terminal(s) 130 may display a target PET image of thesubject. In some embodiments, the terminal(s) 130 may include a mobiledevice 131, a tablet computer 132, a laptop computer 133, or the like,or any combination thereof. For example, the mobile device 131 mayinclude a mobile phone, a personal digital assistant (PDA), a gamingdevice, a navigation device, a point of sale (POS) device, a laptop, atablet computer, a desktop, or the like, or any combination thereof. Insome embodiments, the terminal(s) 130 may include an input device, anoutput device, etc. In some embodiments, the terminal(s) 130 may be partof the processing device 140.

The processing device 140 may process data and/or information obtainedfrom the scanner 110, the storage device 150, the terminal(s) 130, orother components of the imaging system 100. In some embodiments, theprocessing device 140 may be a single server or a server group. Theserver group may be centralized or distributed. For example, theprocessing device 140 may generate a scatter sinogram generator bytraining a preliminary model using a plurality of training samples. Asanother example, the processing device 140 may apply the scattersinogram generator to generate a target scatter sinogram of a subject.In some embodiments, the scatter sinogram generator may be generated bya processing device, while the application of the scatter sinogramgenerator may be performed on a different processing device. In someembodiments, the scatter sinogram generator may be generated by aprocessing device of a system different from the imaging system 100 or aserver different from the processing device 140 on which the applicationof the scatter sinogram generator is performed. For instance, thescatter sinogram generator may be generated by a first system of avendor who provides and/or maintains such a scatter sinogram generator,while the generation of the target scatter sinogram may be performed ona second system of a client of the vendor. In some embodiments, theapplication of the scatter sinogram generator may be performed online inresponse to a request for generating the target scatter sinogram. Insome embodiments, the target scatter sinogram may be generated offline.

In some embodiments, the scatter sinogram generator may be generatedand/or updated (or maintained) by, e.g., the manufacturer of the scanner110 or a vendor. For instance, the manufacturer or the vendor may loadthe scatter sinogram generator into the imaging system 100 or a portionthereof (e.g., the processing device 140) before or during theinstallation of the scanner 110 and/or the processing device 140, andmaintain or update the scatter sinogram generator from time to time(periodically or not). The maintenance or update may be achieved byinstalling a program stored on a storage device (e.g., a compact disc, aUSB drive, etc.) or retrieved from an external source (e.g., a servermaintained by the manufacturer or vendor) via the network 120. Theprogram may include a new model (e.g., a new scatter sinogram generator)or a portion of a model that substitute or supplement a correspondingportion of the model.

In some embodiments, the processing device 140 may be local to or remotefrom the imaging system 100. For example, the processing device 140 mayaccess information and/or data from the scanner 110, the storage device150, and/or the terminal(s) 130 via the network 120. As another example,the processing device 140 may be directly connected to the scanner 110,the terminal(s) 130, and/or the storage device 150 to access informationand/or data. In some embodiments, the processing device 140 may beimplemented on a cloud platform. For example, the cloud platform mayinclude a private cloud, a public cloud, a hybrid cloud, a communitycloud, a distributed cloud, an inter-cloud, a multi-cloud, or the like,or a combination thereof. In some embodiments, the processing device 140may be implemented by a computing device 200 having one or morecomponents as described in connection with FIG. 2.

In some embodiments, the processing device 140 may include one or moreprocessors (e.g., single-core processor(s) or multi-core processor(s)).Merely by way of example, the processing device 140 may include acentral processing unit (CPU), an application-specific integratedcircuit (ASIC), an application-specific instruction-set processor(ASIP), a graphics processing unit (GPU), a physics processing unit(PPU), a digital signal processor (DSP), a field-programmable gate array(FPGA), a programmable logic device (PLD), a controller, amicrocontroller unit, a reduced instruction-set computer (RISC), amicroprocessor, or the like, or any combination thereof.

The storage device 150 may store data, instructions, and/or any otherinformation. In some embodiments, the storage device 150 may store dataobtained from the processing device 140, the terminal(s) 130, and/or thescanner 110. In some embodiments, the storage device 150 may store dataand/or instructions that the processing device 140 may execute or use toperform exemplary methods described in the present disclosure. In someembodiments, the storage device 150 may include a mass storage device, aremovable storage device, a volatile read-and-write memory, a read-onlymemory (ROM), or the like, or any combination thereof. Exemplary massstorage devices may include a magnetic disk, an optical disk, asolid-state drive, etc. Exemplary removable storage devices may includea flash drive, a floppy disk, an optical disk, a memory card, a zipdisk, a magnetic tape, etc. Exemplary volatile read-and-write memory mayinclude a random access memory (RAM). Exemplary RAM may include adynamic RAM (DRAM), a double date rate synchronous dynamic RAM (DDRSDRAM), a static RAM (SRAM), a thyristor RAM (T-RAM), and azero-capacitor RAM (Z-RAM), etc. Exemplary ROM may include a mask ROM(MROM), a programmable ROM (PROM), an erasable programmable ROM (EPROM),an electrically erasable programmable ROM (EEPROM), a compact disk ROM(CD-ROM), and a digital versatile disk ROM, etc. In some embodiments,the storage device 150 may be implemented on a cloud platform asdescribed elsewhere in the disclosure.

In some embodiments, the storage device 150 may be connected to thenetwork 120 to communicate with one or more other components of theimaging system 100 (e.g., the processing device 140, the terminal(s)130). One or more components of the imaging system 100 may access thedata or instructions stored in the storage device 150 via the network120. In some embodiments, the storage device 150 may be part of theprocessing device 140.

It should be noted that the above description of the imaging system 100is intended to be illustrative, and not to limit the scope of thepresent disclosure. Many alternatives, modifications, and variationswill be apparent to those skilled in the art. The features, structures,methods, and other characteristics of the exemplary embodimentsdescribed herein may be combined in various ways to obtain additionaland/or alternative exemplary embodiments. For example, the imagingsystem 100 may include one or more additional components. Additionallyor alternatively, one or more components of the imaging system 100described above may be omitted. As another example, two or morecomponents of the imaging system 100 may be integrated into a singlecomponent.

FIG. 2 is a schematic diagram illustrating exemplary hardware and/orsoftware components of a computing device 200 according to someembodiments of the present disclosure. The computing device 200 may beused to implement any component of the imaging system 100 as describedherein. For example, the processing device 140 and/or the terminal(s)130 may be implemented on the computing device 200, respectively, viaits hardware, software program, firmware, or a combination thereof.Although only one such computing device is shown, for convenience, thecomputer functions relating to the imaging system 100 as describedherein may be implemented in a distributed fashion on a number ofsimilar platforms, to distribute the processing load. As illustrated inFIG. 2, the computing device 200 may include a processor 210, a storagedevice 220, an input/output (I/O) 230, and a communication port 240.

The processor 210 may execute computer instructions (e.g., program code)and perform functions of the processing device 140 in accordance withtechniques described herein. The computer instructions may include, forexample, routines, programs, objects, components, data structures,procedures, modules, and functions, which perform particular functionsdescribed herein. For example, the processor 210 may process image dataobtained from the scanner 110, the terminal(s) 130, the storage device150, and/or any other component of the imaging system 100. In someembodiments, the processor 210 may include one or more hardwareprocessors, such as a microcontroller, a microprocessor, a reducedinstruction set computer (RISC), an application specific integratedcircuits (ASICs), an application-specific instruction-set processor(ASIP), a central processing unit (CPU), a graphics processing unit(GPU), a physics processing unit (PPU), a microcontroller unit, adigital signal processor (DSP), a field programmable gate array (FPGA),an advanced RISC machine (ARM), a programmable logic device (PLD), anycircuit or processor capable of executing one or more functions, or thelike, or any combinations thereof.

Merely for illustration, only one processor is described in thecomputing device 200. However, it should be noted that the computingdevice 200 in the present disclosure may also include multipleprocessors, thus operations and/or method operations that are performedby one processor as described in the present disclosure may also bejointly or separately performed by the multiple processors. For example,if in the present disclosure the processor of the computing device 200executes both operation A and operation B, it should be understood thatoperation A and operation B may also be performed by two or moredifferent processors jointly or separately in the computing device 200(e.g., a first processor executes operation A and a second processorexecutes operation B, or the first and second processors jointly executeoperations A and B).

The storage device 220 may store data/information obtained from thescanner 110, the terminal(s) 130, the storage device 150, and/or anyother component of the imaging system 100. In some embodiments, thestorage device 220 may include a mass storage device, a removablestorage device, a volatile read-and-write memory, a read-only memory(ROM), or the like, or any combination thereof. In some embodiments, thestorage device 220 may store one or more programs and/or instructions toperform exemplary methods described in the present disclosure. Forexample, the storage device 220 may store a program for the processingdevice 140 to execute to generate a scatter sinogram generator.

The I/O 230 may input and/or output signals, data, information, etc. Insome embodiments, the I/O 230 may enable a user interaction with theprocessing device 140. In some embodiments, the I/O 230 may include aninput device and an output device. The input device may includealphanumeric and other keys that may be input via a keyboard, a touchscreen (for example, with haptics or tactile feedback), a speech input,an eye tracking input, a brain monitoring system, or any othercomparable input mechanism. The input information received through theinput device may be transmitted to another component (e.g., theprocessing device 140) via, for example, a bus, for further processing.Other types of the input device may include a cursor control device,such as a mouse, a trackball, or cursor direction keys, etc. The outputdevice may include a display (e.g., a liquid crystal display (LCD), alight-emitting diode (LED)-based display, a flat panel display, a curvedscreen, a television device, a cathode ray tube (CRT), or a touchscreen), a speaker, a printer, or the like, or a combination thereof.

The communication port 240 may be connected to a network (e.g., thenetwork 120) to facilitate data communications. The communication port240 may establish connections between the processing device 140 and thescanner 110, the terminal(s) 130, and/or the storage device 150. Theconnection may be a wired connection, a wireless connection, any othercommunication connection that can enable data transmission and/orreception, and/or any combination of these connections. The wiredconnection may include, for example, an electrical cable, an opticalcable, a telephone wire, or the like, or any combination thereof. Thewireless connection may include, for example, a Bluetooth™ link, aWi-Fi™ link, a WiMax™ link, a WLAN link, a ZigBee™ link, a mobilenetwork link (e.g., 3G, 4G, 5G), or the like, or a combination thereof.In some embodiments, the communication port 240 may be and/or include astandardized communication port, such as RS232, RS485, etc. In someembodiments, the communication port 240 may be a specially designedcommunication port. For example, the communication port 240 may bedesigned in accordance with the digital imaging and communications inmedicine (DICOM) protocol.

FIG. 3 is a schematic diagram illustrating exemplary hardware and/orsoftware components of a mobile device 300 according to some embodimentsof the present disclosure. In some embodiments, one or more components(e.g., a terminal 130 and/or the processing device 140) of the imagingsystem 100 may be implemented on the mobile device 300.

As illustrated in FIG. 3, the mobile device 300 may include acommunication platform 310, a display 320, a graphics processing unit(GPU) 330, a central processing unit (CPU) 340, an I/O 350, a memory360, and storage 390. In some embodiments, any other suitable component,including but not limited to a system bus or a controller (not shown),may also be included in the mobile device 300. In some embodiments, amobile operating system 370 (e.g., iOS™, Android™, Windows Phone™) andone or more applications 380 may be loaded into the memory 360 from thestorage 390 in order to be executed by the CPU 340. The applications 380may include a browser or any other suitable mobile apps for receivingand rendering information relating to image processing or otherinformation from the processing device 140. User interactions with theinformation stream may be achieved via the I/O 350 and provided to theprocessing device 140 and/or other components of the imaging system 100via the network 120.

To implement various modules, units, and their functionalities describedin the present disclosure, computer hardware platforms may be used asthe hardware platform(s) for one or more of the elements describedherein. A computer with user interface elements may be used to implementa personal computer (PC) or any other type of work station or terminaldevice. A computer may also act as a server if appropriately programmed.

FIGS. 4A and 4B are block diagrams illustrating exemplary processingdevices 140A and 140B according to some embodiments of the presentdisclosure. The processing devices 140A and 140B may be exemplaryprocessing devices 140B as described in connection with FIG. 1. In someembodiments, the processing device 140A may be configured to apply ascatter sinogram generator in reconstructing a target PET image of asubject. The processing device 140B may be configured to generate ascatter sinogram generator by model training. In some embodiments, theprocessing devices 140A and 140B may be respectively implemented on aprocessing unit (e.g., a processor 210 illustrated in FIG. 2 or a CPU340 as illustrated in FIG. 3). Merely by way of example, the processingdevices 140A may be implemented on a CPU 340 of a terminal device, andthe processing device 140B may be implemented on a computing device 200.

Alternatively, the processing devices 140A and 140B may be implementedon a same computing device 200 or a same CPU 340. For example, theprocessing devices 140A and 140B may be implemented on a same computingdevice 200.

As shown in FIG. 4A, the processing device 140A may include an obtainingmodule 410, a preliminary scatter sinogram generation module 420, atarget scatter sinogram generation module 430, and a reconstructionmodule 440.

The obtaining module 410 may be configured to obtain PET data of asubject. The PET data may relate to a plurality of coincidence eventsdetected in a PET scan of the subject. The coincidence events mayinclude true coincidence events, random events, scattering events, etc.More descriptions regarding the obtaining of the PET data may be foundelsewhere in the present disclosure. See, e.g., operation 501 in FIG. 5and relevant descriptions thereof.

The preliminary scatter sinogram generation module 420 may be configuredto generate a preliminary scatter sinogram relating to the scatteringevents based on the PET data. A preliminary scatter sinogram refers to ascatter sinogram including information of the scattering events (or aportion thereof), which is simulated from the PET data of the subject.The preliminary scatter sinogram may be used to generate a targetscatter sinogram, which has a higher image quality than the preliminaryscatter sinogram. In some embodiments, the preliminary scatter sinogrammay correspond to a first count of coincidence events of the pluralityof coincidence events, and the target scatter sinogram may correspond toa second count of coincidence events of the coincidence events. Thesecond count may be higher than the first count. More descriptionsregarding the generation of the preliminary scatter sinogram may befound elsewhere in the present disclosure. See, e.g., operation 502 inFIG. 5 and relevant descriptions thereof.

The target scatter sinogram generation module 430 may be configured togenerate the target scatter sinogram relating to the scattering eventsby applying a scatter sinogram generator based on the preliminaryscatter sinogram. The scatter sinogram generator refers to a model or analgorithm configured to generate a target scatter sinogram with adesired image quality based on the preliminary scatter sinogram. In someembodiments, the input of the scatter sinogram generator may include thepreliminary scatter sinogram itself or a combination of the preliminaryscatter sinogram and a true sinogram relating to the true coincidenceevents and the scattering events detected in the PET scan of thesubject. Optionally, the preliminary scatter sinogram and/or the truesinogram may be preprocessed to generate the input of the scattersinogram generator. More descriptions regarding the generation of thetarget scatter sinogram may be found elsewhere in the presentdisclosure. See, e.g., operation 503 in FIG. 5 and relevant descriptionsthereof.

The reconstruction module 440 may be configured to reconstruct thetarget PET image of the subject based on the PET data and the targetscatter sinogram. In some embodiments, the reconstruction module 440 maygenerate the target PET image based on the target scatter sinogram andthe PET data according to a PET image reconstruction algorithm (e.g., anOSEM algorithm, an FBP algorithm, an MLAA algorithm). More descriptionsregarding the reconstruction of the target PET image may be foundelsewhere in the present disclosure. See, e.g., operation 504 in FIG. 5and relevant descriptions thereof.

As shown in FIG. 4B, the processing device 140B may include an obtainingmodule 450 and a model generation module 460.

The obtaining module 450 may be configured to obtain a plurality oftraining samples. Each of the training samples may include a samplepreliminary scatter sinogram and a sample target scatter sinogramrelating to sample scattering events detected in a sample PET scan of asample subject, wherein the sample target scatter sinogram may have ahigher image quality (e.g., a lower noise level) than the samplepreliminary scatter sinogram. More descriptions regarding the obtainingof the training samples may be found elsewhere in the presentdisclosure. See, e.g., operation 801 in FIG. 8 and relevant descriptionsthereof.

The model generation module 460 may be configured to generate thescatter sinogram generator by training a preliminary model using theplurality of training samples. The preliminary model may be of any typeof machine learning model (e.g., a deep learning model). In someembodiments, the training of the preliminary model may be performedbased on one or more gradient descent algorithms, e.g., an Adamoptimization algorithm, a stochastic gradient descent (SGD)+Momentumoptimization algorithm. In some embodiments, the training of thepreliminary model may include one or more training stages, for example,two training stages. More descriptions regarding the generation of thescatter sinogram generator may be found elsewhere in the presentdisclosure. See, e.g., operation 802 in FIG. 8 and relevant descriptionsthereof.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. In someembodiments, the processing device 140A and/or the processing device140B may share two or more of the modules, and any one of the modulesmay be divided into two or more units. For instance, the processingdevices 140A and 140B may share a same obtaining module; that is, theobtaining module 410 and the obtaining module 450 are a same module. Insome embodiments, the processing device 140A and/or the processingdevice 140B may include one or more additional modules, such as astorage module (not shown) for storing data. In some embodiments, theprocessing device 140A and the processing device 140B may be integratedinto one processing device 140.

FIG. 5 is a flowchart illustrating an exemplary process forreconstructing a target PET image of a subject according to someembodiments of the present disclosure. In some embodiments, process 500may be executed by the imaging system 100. For example, the process 500may be implemented as a set of instructions (e.g., an application)stored in a storage device (e.g., the storage device 150, the storagedevice 220, and/or the storage 390). In some embodiments, the processingdevice 140A (e.g., the processor 210 of the computing device 200, theCPU 340 of the mobile device 300, and/or one or more modules illustratedin FIG. 4A) may execute the set of instructions and may accordingly bedirected to perform the process 500.

In 501, the processing device 140A (e.g., the obtaining module 410) mayobtain PET data of a subject.

As used herein, a subject may be biological or non-biological. Forexample, the subject may include a patient (or a portion thereof), ananimal, a man-made object (e.g., a phantom), etc., as describedelsewhere in the present disclosure (e.g., FIG. 1 and the descriptionsthereof). The PET data may relate to a plurality of coincidence eventsdetected in a PET scan of the subject. The plurality of coincidenceevents may include true coincidence events, random events, scatteringevents, etc. The PET data may include the trajectory and/or informationof each of the plurality of coincident events. For example, the PET datamay include a list of LORs, transverse and longitudinal positions of theLORs, or the like, or any combination thereof.

In some embodiments, a scanner (e.g., a PET scanner, a PET-CT scanner, aPET-MR scanner) may be directed to perform a PET scan on the subject toacquire the PET data. The processing device 140A may obtain the PET dataof the subject from the scanner. As another example, the PET data may bepreviously acquired and stored in a storage device (e.g., the storagedevice 150, the storage device 220, and/or the storage 390). Theprocessing device 140A may obtain the PET data of the subject from thestorage device via a network (e.g., the network 120).

In 502, the processing device 140A (e.g., the preliminary scattersinogram generation module 420) may generate a preliminary scattersinogram relating to the scattering events based on the PET data.

As used herein, a preliminary scatter sinogram refers to a scattersinogram including information of the scattering events (or a portionthereof), which is simulated from the PET data of the subject. Thepreliminary scatter sinogram may be used to generate a target scattersinogram relating to the scattering events. A target scatter sinogramrefers to a scatter sinogram that is generated based on the preliminaryscatter sinogram and has a higher image quality than the preliminaryscatter sinogram.

The image quality of a sinogram (e.g., a scatter sinogram) may bemeasured by one or more image quality indexes, such as a noise level, acontrast ratio, a sharpness value, a smoothness, or the like, or anycombination thereof, of the sinogram. The image qualities of twosinograms may be compared by comparing the one or more image qualityindexes. For example, the target scatter sinogram may be regarded ashaving a higher image quality than the preliminary scatter sinogram ifit has a lower noise level than the preliminary scatter sinogram.Additionally or alternatively, the target scatter sinogram may beregarded as having a higher image quality than the preliminary scattersinogram if it has a higher distribution uniformity than the preliminaryscatter sinogram.

In some embodiments, the image quality difference between thepreliminary scatter sinogram and the target scatter sinogram may becaused by various factors. Merely by way of example, the preliminaryscatter sinogram may correspond to a first count of coincidence eventsof the plurality of coincidence events. The first count of coincidenceevents refers to a count of coincidence events that are used to generatethe preliminary scatter sinogram, for example, by using a Monte-Carlosimulation algorithm. In some embodiments, the first count may have alow value that is smaller than a threshold, such as 50 million, 100million, 200 million, 300 million, or the like. For example, the firstcount may be in a range of 10 million to 100 million. The target scattersinogram may correspond to a second count of coincidence events of thecoincidence events, wherein the second count may be higher than thefirst count. The second count of coincidence events refers to apredicted or simulated count of coincidence events that need to be usedto generate a scatter sinogram of a same (or substantially same) imagequality as the target scatter sinogram, for example, by using aMonte-Carlo simulation algorithm. In some embodiments, the second countmay have a high value that is greater than a threshold, such as 0.5billion, 1 billion, 2 billion, 3 billion, or the like. For example, thesecond count may be in a range of 1 billion to 10 billion. In someembodiments, the first count and/or the second count may be a defaultsetting of the imaging system 100 or set by a user manually.Alternatively, the first count and/or the second count may be determinedby the processing device 140A according to an actual need.

Normally, the count of coincidence events used to generate a scattersinogram may affect the image quality of the scattering sinogram and thecalculation resources needed for generating the scatter sinogram. Forexample, if other conditions remain the same, increasing the count ofcoincidence events may result in a scatter sinogram having a higherimage quality (e.g., a lower noise level). However, the greater thecount of coincidence events is, the more computational resources (e.g.,a storage space) and/or the longer time may be needed for generating thescatter sinogram. Increasing the count of coincidence events may callfor more advanced computing devices, and/or cause a longer scanningand/or image reconstruction time in clinical examinations and/ortreatment. The systems and methods disclosed herein may first generatethe preliminary scatter sinogram of a relatively low image quality(e.g., a high noise level) using a small portion of the coincidenceevents, and then generate, based on the preliminary scatter sinogram,the target scatter sinogram with a higher image quality (e.g., a reducednoise level) than the preliminary scatter sinogram, thereby achieving animproved reconstruction efficiency by taking advantage of a reducedcomputational complexity and/or resources needed for generating thepreliminary scatter sinogram.

It should be noted that the difference in the counts of coincidenceevents is merely an exemplary factor that induces the difference in theimage quality between the preliminary scatter sinogram and the scattersinogram generator. One or more other factors, such as, a computationtime may also affect the image quality of the preliminary scattersinogram. The systems and methods may be applied to reduce or eliminatethe effect of one or more other factors to improve the image quality.

In some embodiments, to generate the preliminary scatter sinogram, theprocessing device 140A may generate a preliminary PET image of thesubject based on the PET data. The preliminary PET image may need toundergo an image correction, a noise reduction, or the like, to moreaccurately indicate positioning information of the radioactive tracerisotope injected into the subject. The preliminary PET image may begenerated based on the PET data according to a PET image reconstructionalgorithm, such as an ordered subsets expectation maximization (OSEM)algorithm, a maximum likelihood (MLEM) algorithm, and a filter backprojection (FBP) algorithm. The processing device 140A may furthergenerate the preliminary scatter sinogram based on the preliminary PETimage. For example, the processing device 140A may generate thepreliminary scatter sinogram by applying a scatter sinogram estimationtechnique, such as, a convolution algorithm, a hardware dual-energywindows algorithm, an analytical modeling algorithm, a Monte-CarloSimulation algorithm, or the like, or any combination thereof. In someembodiments, the processing device 140A may generate the preliminaryscatter sinogram by performing the process 600 as in connection withFIG. 6.

In 503, the processing device 140A (e.g., the target scatter sinogramgeneration module 430) may generate a target scatter sinogram relatingto the scattering events by applying a scatter sinogram generator basedon the preliminary scatter sinogram.

The scatter sinogram generator refers to a model or an algorithmconfigured to generate a target scatter sinogram with a desired imagequality based on the preliminary scatter sinogram. For example, thescatter sinogram generator may include a machine-learning model (e.g., atrained neural network) for generating a scatter sinogram with a desiredimage quality based on its input.

In some embodiments, the input of the scatter sinogram generator mayinclude the preliminary scatter sinogram itself or a combination of thepreliminary scatter sinogram and a true sinogram relating to the truecoincidence events and the scattering events detected in the PET scan ofthe subject. The preliminary scatter sinogram, which is generated from aportion of the coincidence events, may lose some information relating tothe scattering events. The true sinogram may include the originalinformation relating to the scattering events, and serve as an input incombination with the preliminary scatter sinogram so as to improve theaccuracy of the target scatter sinogram to be generated, and in turn thePET image reconstruction accuracy. Optionally, the processing device140A may preprocess the preliminary scatter sinogram and/or the truesinogram to generate the input of the scatter sinogram generator. Thepreprocessing of the preliminary scatter sinogram and/or the truesinogram may include an image denoising, an image enhancement, an imagesmoothing, an image transformation, an image resampling, an imagenormalization, an image concatenation, or the like, or a combinationthereof.

Merely by way of example, the processing device 140A may generate anormalized preliminary scatter sinogram and a normalized true sinogramby normalizing the preliminary scatter sinogram and the true sinogram,respectively. In some embodiments, the normalization of a sinogram maybe performed such that pixel (or voxel) values of the normalizedsinogram (e.g., the normalized preliminary scatter sinogram, thenormalized true sinogram) may be within a preset range. In someembodiments, the scatter sinogram generator may be trained using aplurality of sample preliminary scatter sinograms and a plurality ofsample true sinograms. The processing device 140A may normalize thepreliminary scatter sinogram and the true sinogram according to Equation(1) as below, respectively:

I′=I−μ/σ,  (1)

where I may represent a sinogram (e.g., the preliminary scattersinogram, the true sinogram), I′ may represent a normalized sinogram, μmay represent a mean value of sample sinograms with respect to thesinogram used in the training of the scatter sinogram generator, and σmay represent a standard deviation of the sample sinograms with respectto the normalized sinogram. For instance, in the normalization of thepreliminary scatter sinogram, I′ may represent the normalizedpreliminary scatter sinogram, μ may represent a mean value of samplepreliminary scatter sinograms on the basis of which the normalization isperformed, and σ may represent a standard deviation of the samplepreliminary scatter sinograms. As another example, in the normalizationof the true sinogram, I′ may represent the normalized true sinogram, μmay represent a mean value of the sample true sinograms, and σ mayrepresent a standard deviation of the sample true sinograms.

The processing device 140A may further generate a concatenated sinogramby concatenating the normalized preliminary scatter sinogram and thenormalized true sinogram, wherein the concatenated sinogram may bedesignated as the input of the scatter sinogram generator. In someembodiments, the normalized preliminary scatter sinogram and thenormalized true sinogram may be concatenated along a preset dimension(e.g., a channel dimension). For example, the normalized preliminaryscatter sinogram and normalized true sinogram may both be or include2-dimensional images having a first dimension and a second dimension.The normalized preliminary scatter sinogram and normalized true sinogrammay be concatenated along a third dimension to generate the concatenatedsinogram (e.g., a 3-dimensional sinogram including the first, second andthird dimensions).

The processing device 140A may then generate the target scatter sinogramby processing the concatenated sinogram using the scatter sinogramgenerator. For example, the concatenated sinogram may be inputted intothe scatter sinogram generator, and the scatter sinogram generator mayoutput an initial scatter sinogram. The target scatter sinogram may begenerated by performing a linear transformation on the initial scattersinogram. For example, the initial scatter sinogram may be denormalizedaccording to Equation (2) as below:

I _(t) =I′ _(i)*σ_(gt)+μ_(gt),  (2)

where I_(t) may represent the target scatter sinogram, I′_(i) mayrepresent the initial scatter sinogram, σ_(gt) may represent a standarddeviation of a plurality of sample target scatter sinograms used in thetraining of the scatter sinogram generator, and μ_(gt) may represent amean value (e.g., a gray mean value) of the sample target scattersinograms used in the training of the scatter sinogram generator.

It should be noted that the above descriptions of the input and outputof the scatter sinogram generator are merely provided for the purposesof illustration, and not intended to limit the scope of the presentdisclosure. For example, the input of the scatter sinogram generator maymerely include the preliminary scatter sinogram or the preprocessedpreliminary scatter sinogram. As another example, the imagenormalization of the preliminary scatter sinogram and/or the truesinogram may be omitted. As yet another example, the scatter sinogramgenerator may output the target scatter sinogram directly.

In some embodiments, the scatter sinogram generator may be of any typeof machine-learning model. For example, the scatter sinogram generatormay include a convolutional neural network (CNN) model (e.g., a fullyconvolutional model, V-net model, a U-net model, an AlexNet model, anOxford Visual Geometry Group (VGG) model, a ResNet model), a generativeadversarial network (GAN) model (e.g., a pix2pix model, a WassersteinGAN (WGAN) model), a recurrent network, an ordinary deep neural network,a deep belief network, or the like, or any combination thereof.Optionally, the scatter sinogram generator may include one or morecomponents for feature extraction and/or feature combination, such as aconvolutional block, a skip-connection, a residual block, or the like,or any combination thereof. In some embodiments, the scatter sinogramgenerator may have a same configuration as or a similar configuration toa preliminary model 1000 as shown in FIG. 10.

In some embodiments, the processing device 140A (e.g., the obtainingmodule 410) may obtain the scatter sinogram generator from one or morecomponents of the imaging system 100 (e.g., the storage device 150, theterminals(s) 130) or an external source via a network (e.g., the network120). For example, the scatter sinogram generator may be previouslytrained by a computing device (e.g., the processing device 140B), andstored in a storage device (e.g., the storage device 150, the storagedevice 220, and/or the storage 390) of the imaging system 100. Theprocessing device 140A may access the storage device and retrieve thescatter sinogram generator. In some embodiments, the scatter sinogramgenerator may be generated according to a machine learning algorithm.The machine learning algorithm may include but not be limited to anartificial neural network algorithm, a deep learning algorithm, adecision tree algorithm, an association rule algorithm, an inductivelogic programming algorithm, a support vector machine algorithm, aclustering algorithm, a Bayesian network algorithm, a reinforcementlearning algorithm, a representation learning algorithm, a similarityand metric learning algorithm, a sparse dictionary learning algorithm, agenetic algorithm, a rule-based machine learning algorithm, or the like,or any combination thereof. The machine learning algorithm used togenerate the scatter sinogram generator may be a supervised learningalgorithm. In some embodiments, the scatter sinogram generator may begenerated by a computing device (e.g., the processing device 140B) byperforming a process (e.g., process 800) for generating a scattersinogram generator disclosed herein.

In 504, the processing device 140A (e.g., the reconstruction module 440)may reconstruct the target PET image of the subject based on the PETdata and the target scatter sinogram.

In some embodiments, the target PET image may have less noise than thepreliminary PET image of the subject as described in connection with502. For example, during the reconstruction of the target PET image, ascatter correction may be performed based on the target scattersinogram, and the resulting target PET image may have less scatteringnoises than the preliminary PET image. As another example, during thereconstruction of the target PET image, a random correction may beperformed based on a delay sinogram (which will be described in detailin connection with FIG. 6), and the resulting target PET image may haveless random noise than the preliminary PET image. The random correctionmay be performed according to one or more random correction techniques,such as a delayed window technique, a single event technique, a tailfitting technique, or the like, or any combination thereof.

In some embodiments, the processing device 140A may generate the targetPET image based on the target scatter sinogram and the PET dataaccording to a PET image reconstruction algorithm (e.g., an OSEMalgorithm, an FBP algorithm, an MLAA algorithm). For example, theprocessing device 140A may generate a scatter correction sinogram bycorrecting the target scatter sinogram. The processing device 140A mayfurther perform an interpolation on a sampling space of the PET databased on the scatter correction sinogram to generate the target PETimage. In some embodiments, the processing device 140A may perform oneor more operations in process 700 as described in connection with FIG. 7to reconstruct the target PET image.

It should be noted that the above description regarding the process 500is merely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations or modifications may be madeunder the teachings of the present disclosure. However, those variationsand modifications do not depart from the scope of the presentdisclosure. In some embodiments, one or more operations may be added oromitted. For example, the process 500 may include an additionaloperation to transmit the target PET image to a terminal (e.g., aterminal 130) for display.

FIG. 6 is a flowchart illustrating an exemplary process for generating apreliminary scatter sinogram according to some embodiments of thepresent disclosure. In some embodiments, process 600 may be executed bythe imaging system 100. For example, the process 600 may be implementedas a set of instructions (e.g., an application) stored in a storagedevice (e.g., the storage device 150, the storage device 220, and/or thestorage 390). In some embodiments, the processing device 140A (e.g., theprocessor 210 of the computing device 200, the CPU 340 of the mobiledevice 300, and/or one or more modules illustrated in FIG. 4A) mayexecute the set of instructions and may accordingly be directed toperform the process 600. In some embodiments, one or more operations ofthe process 600 may be performed to achieve at least part of operation502 as described in connection with FIG. 5.

In 601, the processing device 140A (e.g., the preliminary scattersinogram generation module 420) may generate a prompt sinogram and adelay sinogram based on the PET data of the subject.

As described in connection with 501, the PET data may relate to aplurality of coincidence events, including true coincidence events,random events, and scattering events, detected in a PET scan of thesubject. In some embodiments, the prompt sinogram may be generated byidentifying a pair of photons that impinge on and interact with twoscintillators of two detector units within a coincidence time window. Insome embodiments, the prompt sinogram may include information relating(mainly) to the coincidence events (e.g., true coincidence events,random events, and scattering events) detected in the PET scan of thesubject. The delay sinogram may be generated by identifying a pair ofphotons that impinge on and interact with two scintillators of twodetector units within a delay time window (e.g., a time window that isdelayed by a certain period relative to the detection of the firstphoton of the pair of photons). In some embodiments, the delay sinogrammay include information relating (mainly) to random events detected inthe PET scan of the subject.

In 602, the processing device 140A (e.g., the preliminary scattersinogram generation module 420) may determine a true sinogram based onthe prompt sinogram and the delay sinogram. For example, the processingdevice 140A may subtract the delay sinogram from the prompt sinogram togenerate the true sinogram. The generated true sinogram may includeinformation relating to the true coincidence events and the scatteringevents.

In 603, the processing device 140A (e.g., the preliminary scattersinogram generation module 420) may generate the preliminary PET imageof the subject based on the true sinogram.

The preliminary PET image may be generated based on the true sinogramusing a PET image reconstruction algorithm as described elsewhere inthis disclosure (e.g., operation 502 and the relevant descriptions). Insome alternative embodiments, the processing device 140A may generatethe preliminary PET image based on the prompt sinogram. The preliminaryPET image generated based on the true sinogram may have less randomnoise than a preliminary PET image generated based on the promptsinogram because information relating to the random events (e.g.,represented in the form of the delay sinogram) is removed in thegeneration of the true sinogram.

In 604, the processing device 140A (e.g., the preliminary scattersinogram generation module 420) may generate the preliminary scattersinogram based on the preliminary PET image.

As described in connection with 501, the preliminary scatter sinogrammay be generated based on the preliminary PET image according to one ormore of various scatter sinogram estimation techniques. For illustrationpurposes, the generation of the preliminary scatter sinogram using aMonte-Carlo Simulation algorithm is described hereinafter.

For example, the processing device 140A may obtain an attenuation map ofthe subject or a portion thereof. The attenuation map may include aplurality of attenuation coefficients of a plurality of physical pointsof the subject. In some embodiments, the attenuation map may begenerated by performing a CT scan on the subject. During the CT scan ofthe subject, X-rays may be emitted toward the subject or a portionthereof, and different parts of the subject may have differentabsorptions of the X-rays. A CT image may be generated based onattenuation values of the X-rays after being absorbed by different partsof the subject. The attenuation map may be generated based on the CTimage. In some embodiments, the attenuation map may be generated by theprocessing device 140A based on the CT image or CT scan data acquired inthe CT scan. Alternatively, the attenuation map may be previouslygenerated and stored in a storage device (e.g., the storage device 150,the storage device 220, and/or the storage 390). The processing device140A may obtain the attenuation map of the subject from the storagedevice via a network (e.g., the network 120).

The processing device 140A may further generate the preliminary scattersinogram based on the preliminary PET image and the attenuation map byapplying the Monte-Carlo Simulation algorithm. For example, theprocessing device 140A may utilize a Monte-Carlo simulation tool (e.g.,a software, an application, a toolkit) to process the preliminary PETimage based on the attenuation map. Exemplary Monte-Carlo simulationtools may include an Electron Gamma Shower (EGSnrc), a Monte-CarloN-Particle Transport (MCNP), a GEometry ANd Tracking (Geant4), a DosePlanning Method (DPM) tool, a Voxel-based Monte Carlo (VMC), a VMC++, orany other simulation tools designed according to the Monte-Carlosimulation algorithm, or any combination thereof. Additionally oralternatively, the preliminary scatter sinogram may be generated byusing one or more other scatter correction techniques, for example, ananalytical modeling technique, a source modulation algorithm, etc.

In some embodiments, the preliminary scatter sinogram may have arelatively lower image quality than the target scatter sinogram asdescribed in connection with FIG. 5. In order to generate such apreliminary scatter sinogram, a total count of coincidence events thatis needed for the Monte-Carlo Simulation, i.e., the first count ofcoincidence events as described in connection with FIG. 5, may bedetermined. The Monte-Carlo Simulation may be terminated until the firstcount of coincidence events is processed in the generation of thepreliminary scatter sinogram. The first count may have a low valuesmaller than a threshold (e.g., 100 million) so that the preliminaryscatter sinogram may have a low image quality (e.g., a high noiselevel).

In some embodiments, the image quality of the preliminary scattersinogram may be measured by a spatial distribution of the estimatedscattering events (or annihilated photons) in the preliminary scattersinogram. For example, the spatial distribution of the estimatedscattering events in the preliminary scatter sinogram may be measuredby, for example, a deviation between the counts of the estimatedscattering events at different positions in the subject. Merely by wayof example, it is assumed that the tracer isotope is (substantially)evenly distributed in the subject, if the deviation is less than acertain percentage (e.g., 5%) of the total count of the estimatedscattering events (or annihilated photons), the preliminary scattersinogram may be considered as having a high image quality (e.g., a lownoise level); if the deviation is greater than or equal to the certainpercentage of the total count of the estimated scattering events (orannihilated photons), the preliminary scatter sinogram may be consideredas having a low image quality (e.g., a high noise level).

It should be noted that the above description regarding the process 600is merely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations or modifications may be madeunder the teachings of the present disclosure. However, those variationsand modifications do not depart from the scope of the presentdisclosure. In some embodiments, one or more operations may be added oromitted. In some embodiments, the processing device 140A may merelygenerate or obtain the prompt sinogram in 601. Operation 602 may beomitted, and the processing device 140A may generate the preliminary PETimage of the subject based on the prompt sinogram in 603. Optionally,random correction may be performed in the reconstruction of the targetPET image to eliminate or reduce the effect of the random events.

FIG. 7 is a flowchart illustrating an exemplary process forreconstructing a target PET image according to some embodiments of thepresent disclosure. In some embodiments, process 700 may be executed bythe imaging system 100. For example, the process 700 may be implementedas a set of instructions (e.g., an application) stored in a storagedevice (e.g., the storage device 150, the storage device 220, and/or thestorage 390). In some embodiments, the processing device 140A (e.g., theprocessor 210 of the computing device 200, the CPU 340 of the mobiledevice 300, and/or one or more modules illustrated in FIG. 4A) mayexecute the set of instructions and may accordingly be directed toperform the process 700. In some embodiments, one or more operations ofthe process 700 may be performed to achieve at least part of operation504 as described in connection with FIG. 5.

In 701, the processing device 140A (e.g., the reconstruction module 440)may generate a scatter correction sinogram by correcting the targetscatter sinogram.

In some embodiments, the correction of the target scatter sinogram mayinclude, for example, a statistical scaling operation, a geometriccorrection, or the like, or any combination thereof. For example, astatistical scaling operation may be performed on the target scattersinogram such that the scatter correction sinogram has a same orsubstantially same computation amount as the true sinogram. Acomputational cost of a sinogram may be measured by, for example, theamount of coincidence events that the sinogram relates to, the pixel (orvoxel) value range in the sinogram, or the like, or a combinationthereof. For example, the statistical scaling operation may be performedsuch that the pixel (or voxel) value range of the scatter correctionsinogram is substantially the same as that of the true sinogram.

In some embodiments, the processing device 140A may determine a scalingcoefficient and perform the statistical scaling operation by multiplyingthe target scatter sinogram by the scaling coefficient. For example, thescaling coefficient may be determined by dividing the computational costof the target scatter sinogram by the computational cost of the truesinogram. Alternatively, the processing device 140A may determine aformula representing a relationship between the target scatter sinogramand the true sinogram according to a least-squares fit algorithm. Theformula may include one or more fitting parameters, and the processingdevice 140A may perform the statistical scaling operation on the targetscatter sinogram based on the fitting coefficients or the formula. Insome embodiments, the processing device 140A may perform a geometriccorrection on the target scatter sinogram based on geometric data in thetarget scatter sinogram and/or geometric data in the PET data.

In 702, the processing device 140A (e.g., the reconstruction module 440)may generate an updated PET image of the subject based on the scattercorrection sinogram and the PET data.

In some embodiments, the updated PET image may be generated byperforming an initial fast reconstruction on PET data and the scattercorrection sinogram according to a PET image reconstruction algorithm asdescribed elsewhere in this disclosure (e.g., the operation 502 and therelevant descriptions). The updated PET image may have a relativelylower image quality (e.g., a lower image resolution) than the target PETimage to be generated.

In 703, the processing device 140A (e.g., the reconstruction module 440)may generate a final scatter correction sinogram by iteratively updatingthe scatter correction sinogram based on the updated PET image.

For example, the updating of the scatter correction sinogram may includeone or more iterations. In each current iteration, the processing device140A may generate a first scatter correction sinogram based on theupdated PET image generated in a previous iteration. For example, theprocessing device 140A may generate an updated preliminary scattersinogram by performing operation 604 on the updated PET image generatedin a previous iteration. The processing device 140A may further generatethe first scatter correction sinogram by performing operation 503 on theupdated preliminary scatter sinogram.

The processing device 140A may determine whether a termination conditionis satisfied in the current iteration. Exemplary termination conditionsmay include that a certain count of iterations is performed, theiteration operation lasts for more than a certain period, the firstscatter correction sinogram has a desired image quality, etc. Forexample, the termination condition may be that more than a certain countof iterations is performed. The certain count may be a default value ofthe imaging system 100, or set by a user manually, or determined by theprocessing device 140A according to an actual need. The certain countmay have any positive integer, for example, within a range from 2 to 20,or 3 to 8. For example, the certain count may be 4. In some embodiments,whether the termination condition is satisfied may be determinedmanually by a user. For example, the first scatter correction sinogrammay be displayed on an interface implemented on, for example, theterminal 130, and the user may input an evaluation result regardingwhether the first scatter correction sinogram has a desired imagequality.

If it is determined that the termination condition is satisfied in thecurrent iteration, the processing device 140A may designate the firstscatter correction sinogram as the final scatter correction sinogram ofthe subject. If it is determined that the termination condition is notsatisfied in the current iteration, the processing device 140A mayproceed to a next iteration to update the first scatter correctionsinogram until the termination condition is satisfied. By iterativelyupdating the first scatter correction sinogram, a final scattercorrection sinogram having an improved accuracy may be generated so asto improve the accuracy of the target PET image to be reconstructed.

In 704, the processing device 140A (e.g., the reconstruction module 440)may reconstruct the target PET image of the subject based on the PETdata and the final scatter correction sinogram.

In some embodiments, the target PET image of the subject may begenerated by reconstructing the PET data and the final scattercorrection sinogram according to a PET image reconstruction algorithm.In some embodiments, the reconstruction of the target PET image may costmore time than the reconstruction of the updated PET image as describedin connection with 701. The target PET image may have a higher imagequality (e.g., a higher image resolution) than the updated PET image. Insome embodiments, the difference in the reconstruction time and/or theimage quality between the target PET image and the updated PET image maybe caused by the difference in the sample precision (e.g., measured by acomputational cost) between the scatter correction sinogram used in thereconstruction of the updated PET image and the final scatter correctionsinogram used in the reconstruction of the target PET image. Forexample, the computational cost of the final scatter correction sinogrammay be higher than that of the scatter correction sinogram, so that thereconstruction of the target PET image may cost more time, and thetarget PET image may have a higher image resolution than the updated PETimage.

It should be noted that the above description regarding the process 700is merely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations or modifications may be madeunder the teachings of the present disclosure. However, those variationsand modifications do not depart from the scope of the presentdisclosure. In some embodiments, one or more operations may be added oromitted. In some embodiments, operation 701 may be omitted. In 702, theprocessing device 140A may generate the updated PET image of the subjectbased on the original target scatter sinogram and the PET data. In 703,the processing device 140A may generate the final scatter correctionsinogram based on the target scatter sinogram and the updated PET image(e.g., by iteratively updating the target scatter sinogram based on theupdated PET image).

FIG. 8 is a flowchart illustrating an exemplary process for generating ascatter sinogram generator according to some embodiments of the presentdisclosure. In some embodiments, process 800 may be executed by theimaging system 100. For example, the process 800 may be implemented as aset of instructions (e.g., an application) stored in a storage device(e.g., the storage device 150, the storage device 220, and/or thestorage 390). In some embodiments, the processing device 140B (e.g., theprocessor 210 of the computing device 200, the CPU 340 of the mobiledevice 300, and/or one or more modules illustrated in FIG. 4B) mayexecute the set of instructions and may accordingly be directed toperform the process 800. In some embodiments, the process 800 may beperformed by another device or system other than the imaging system 100,e.g., a device or system of a vendor of a manufacturer. For illustrationpurposes, the implementation of the process 800 by the processing device140B is described as an example.

In 801, the processing device 140B (e.g., the obtaining module 450) mayobtain a plurality of training samples. Each of the training samples mayinclude a sample preliminary scatter sinogram and a sample targetscatter sinogram relating to sample scattering events detected in asample PET scan of a sample subject, wherein the sample target scattersinogram may have a higher image quality (e.g., a lower noise level)than the sample preliminary scatter sinogram.

For a training sample, the corresponding sample subject may be of thesame type as or a different type from the subject as described inconnection with 501. For example, the subject may be the head of apatient, and the sample subject may be the head of another patient or aman-made object (e.g., a phantom). In some embodiments, sample PET dataof the sample subject may be collected in a sample PET scan of thesample subject. The sample PET data may relate to a plurality of samplecoincidence events detected in the sample PET scan. The samplepreliminary scatter sinogram and the sample target scatter sinogram maybe simulated from the sample PET data with different simulationconditions. The different simulation conditions may include, forexample, different computation amounts (e.g., different counts ofcoincidence events used in simulation), different computation times,different spatial distributions of coincidence event used in thesimulation, or any other conditions that may induce a difference betweenthe image qualities of the sample preliminary scatter sinogram and thesample target scatter sinogram.

Merely by way of example, the sample preliminary scatter sinogram may besimulated based on a third count of sample coincidence events of thesample plurality of coincidence events according to a scatter sinogramestimation technique (e.g., a Monte-Carlo simulation algorithm). Thesample target scatter sinogram may be simulated based on a fourth countof sample coincidence events of the plurality of sample coincidenceevents according to a scatter sinogram estimation technique (e.g., aMonte-Carlo simulation algorithm), wherein the fourth count may behigher than the third count. In some embodiments, the third count andthe fourth count may be equal to or substantially equal to the firstcount and the second count, respectively, as described in connectionwith FIG. 5.

In some embodiments, each of the plurality of training samples mayfurther include a sample true sinogram relating to sample truecoincidence events and sample scattering events detected in thecorresponding sample PET scan. The sample true sinogram of a trainingsample may be generated in a similar manner as the generation of thetrue sinogram as described in connection with operations 601 and 602.For example, for a training sample, the processing device 140B maygenerate a sample prompt sinogram and a sample delay sinogram based onthe sample PET data of the training sample. The processing device 140Bmay further determine the sample true sinogram based on the sampleprompt sinogram and the sample delay sinogram. For illustrationpurposes, the following description is described with reference totraining samples each of which includes a sample preliminary scattersinogram, a sample target scatter sinogram, and a sample true sinogram.This is not intended to limit the scope of the present disclosure, andeach training sample may not include a true sinogram according to somealternative embodiments of the present disclosure.

In some embodiments, the processing device 140B may preprocess at leastone of the training samples. For example, for a training sample, theprocessing device 140B may normalize one or more of the samplepreliminary scatter sinogram, the sample target scatter sinogram, andthe sample true sinogram of the training sample. The normalization of asinogram may be performed in a similar manner as that of the preliminaryscatter sinogram and the true sinogram as described in connection with503, for example, according to Equation (1). The processing device 140Bmay further generate a cropped sample preliminary scatter sinogram, acropped sample target scatter sinogram, a cropped sample true sinogramby cropping the normalized sample preliminary scatter sinogram, thenormalized sample target scatter sinogram, and the normalized sampletrue sinogram of the training sample, respectively. In some embodiments,the cropped sample preliminary scatter sinogram may include one or morefirst image crops cropped from the sample preliminary scatter sinogram.In some embodiments, the cropped sample target scatter sinogram mayinclude one or more second image segments cropped from the sample targetscatter sinogram. In some embodiments, the cropped sample true sinogrammay include one or more third image segments cropped from the sampletrue sinogram. The position(s) of the first image segments in the samplepreliminary scatter sinogram may be the same as the position(s) of thesecond image segment(s) in the sample target scatter sinogram and theposition(s) of the third image segment(s) in the sample true sinogram.In some embodiments, the position of a first image segment in a firstsinogram being the same as the position of a second image segment in asecond sinogram indicates that the first image segment and the secondimage segment correspond to a same position in the sinogram domain.Additionally or alternatively, each of the first, second, and thirdimage segments may have a same size, for example, [64, 64, 5] or [64,64, 1].

The processing device 140B may further generate a sample concatenatedsinogram by concatenating the cropped sample preliminary scattersinogram and the cropped sample true sinogram. In some embodiments, thecropped sample preliminary scatter sinogram and the cropped sample truesinogram may be concatenated along a preset dimension (e.g., a channeldimension). For example, a certain count (e.g., N) of 2D slices of thecropped sample preliminary scatter sinogram may be concatenated with thesame count (e.g., N) of 2D slices of the cropped sample true sinogramalong the channel dimension. N may be equal to any positive integer,such as, 1, 3, 5, 10, 15, or the like. In some embodiments, N may be apositive odd number. Merely by way of example, 5 2D slices of thecropped sample preliminary scatter sinogram and 5 2D slices of thecropped sample true sinogram, each of which has a size of [64, 64], maybe concatenated along the channel dimension to generate a sampleconcatenated sinogram having a size of [64, 64, 10]. In such cases, thepreprocessed training sample may include the sample concatenatedsinogram having a size of [64, 64, 10] and the cropped sample truesinogram having a size of, for example, [64, 64, 1]. In someembodiments, the concatenation of the 2D slices of the cropped samplepreliminary scatter sinogram and the cropped sample true sinogram alonga channel dimension may also be referred to as a 2.5D concatenation.

In some embodiments, a (preprocessed) training sample (or a portionthereof) may be previously generated and stored in a storage device(e.g., the storage device 150, the storage device 220, and/or thestorage 390). The processing device 140B may obtain the (preprocessed)training sample (or a portion thereof) from the storage device via anetwork (e.g., the network 120). Additionally or alternatively, a(preprocessed) training sample (or a portion thereof) may be generatedby the processing device 140B based on the sample PET data of thetraining sample. In some embodiments, the count of the (preprocessed)training samples may be equal to any positive integer. For example, M (apositive integer) training samples, each of which includes one samplepreliminary scatter sinogram, one sample target scatter sinogram, andone sample true sinogram, may be obtained for model training.

In 802, the processing device 140B (e.g., the model generation module460) may generate the scatter sinogram generator by training apreliminary model using the plurality of (preprocessed) trainingsamples.

The preliminary model may be of any type of machine learning model(e.g., a deep learning model), for example, a fully convolutionalnetwork (FCN) (e.g., a V-net model, a U-net model), a generativeadversarial network (GAN) (e.g., a pix2pix model, a Wasserstein GAN(WGAN) model), a recurrent network, an ordinary deep neural network, anda deep belief network, or the like, or any combination thereof. In someembodiments, the preliminary model may have a same configuration as or asimilar configuration to a neural network 1000 as shown in FIG. 10.

In some embodiments, the preliminary model may include one or more modelparameters. For example, the preliminary model may be a U-net model andexemplary model parameters of the preliminary model may include thenumber (or count) of layers, the number (or count) of kernels, a kernelsize, a pooling size, a stride, a loss function, or the like, or anycombination thereof. Before training, the model parameter(s) may havetheir respective initial values. For example, the processing device 140Bmay initialize parameter value(s) of the model parameter(s) of thepreliminary model.

In some embodiments, the training of the preliminary model may beperformed based on one or more gradient descent algorithms. Exemplarygradient descent algorithms may include an Adam optimization algorithm,a stochastic gradient descent (SGD)+Momentum optimization algorithm, aNesterov accelerated gradient (NAG) algorithm, an Adaptive Gradient(Adagrad) algorithm, an Adaptive Delta (Adadelta) algorithm, a Root MeanSquare Propagation (RMSprop) algorithm, an AdaMax algorithm, a Nadam(Nesterov-accelerated Adaptive Moment Estimation) algorithm, an AMSGrad(Adam+SGD) algorithm, or the like, or any combination thereof.

In some embodiments, the training of the preliminary model may includeone or more training stages. Merely by way of example, the training ofthe preliminary model may include two training stages in which differentgradient descent algorithms are adopted. In the first training stage,the processing device 140B may generate a preliminary scatter sinogramgenerator by training the preliminary model using the plurality oftraining samples according to a first gradient descent algorithm, suchas an Adam optimization algorithm. In the second training stage, theprocessing device 140B may generate the scatter sinogram generator bytraining the preliminary scatter sinogram generator using the trainingsamples according to a second gradient descent algorithm. The secondgradient descent algorithm may be different from the first gradientdescent algorithm. For example, the second gradient descent algorithmmay be a stochastic gradient descent (SGD)+Momentum optimizationalgorithm. As used herein, the “using the training samples” refers tousing at least a portion of the training samples (or preprocessedtraining samples). In some embodiments, a same set of training samplesor different sets of training samples may be used in the two trainingstages. For example, the two training stages may utilize two differentsub-sets of the training samples. By using the first and second gradientdescent algorithms to train the preliminary model with two trainingstages, the preliminary model may converge more quickly and/or thegenerated scatter sinogram generator may have an improved accuracyand/or reliability.

Additionally or alternatively, different learning rate strategies may beadopted for the two training stages in training the preliminary model.Exemplary learning rate strategies may include a learning rate rangetest (LR Range Test) technique, a cycle learning rate technique, a fixedlearning rate technique, a learning rate decay technique, a bisectionline search technique, a backing line search technique, a cosineannealing technique, an SGD with warm restarts (SGDR), a differentiallearning rate technique, or the like, or any combination thereof.

Merely by way of example, before the first training stage, theprocessing device 140B may determine an optimal learning rate withrespect to the first gradient descent algorithm according to an LR RangeTest technique. For example, the processing device 140B may determine aninitial learning rate, which has a relatively small value. Theprocessing device 140B may update the preliminary model based on thetraining samples by performing a plurality of first iterations, duringwhich the learning rate may start with the initial learning rate andincrease after each first iteration. The processing device 140B mayfurther determine a value of a loss function of the preliminary model ineach of the first iterations, and generate an LR Range Test diagrambased on the value of the loss function in each first iteration. The LRRange Test diagram may be represented in a two-dimensional (2D)coordinate system, wherein a horizontal axis represents the learningrate and a longitudinal axis represents the loss function. Generally,the LR Range Test diagram may be divided into three sub-ranges,including a first sub-range in which the loss function has asubstantially constant value due to the low learning rate in the firstsub-range, a second sub-range in which the loss function convergesrapidly, and a third sub-range in which the loss function diverges dueto the high learning rate in the third sub-range. The processing device140B may then identify a point that corresponds to the minimum value ofthe loss function in the LR Range Test diagram, and designate thelearning rate of the identified point as the optimal learning rate. Insome embodiments, the optimal learning rate may be set by a usermanually or determined by the processing device 140B according to anactual need.

In the first training stage, the processing device 140B may generate thepreliminary scatter sinogram generator by training the preliminary modelusing the plurality of training samples according to the first gradientdescent algorithm and the optimal learning rate. For example, theprocessing device 140B may generate the preliminary scatter sinogramgenerator by performing one or more second iterations to iterativelyupdate the model parameter(s) of the preliminary model. The optimallearning rate may be used as a constant learning rate or an initiallearning rate that may be adaptively adjusted in the training of thepreliminary model.

Before the second training stage, the processing device 140B maydetermine a learning rate range with respect to the second gradientdescent algorithm based on the optimal learning rate (or the LR RangeTest diagram). The lowest learning rate of the learning rate range maybe smaller than the optimal learning rate, and the highest learning rateof the learning rate range may be greater than the optimal learningrate. The processing device 140B may generate the scatter sinogramgenerator by training the preliminary scatter sinogram generator usingthe plurality of training samples according to the second gradientdescent algorithm, the learning rate range, and a cycle learning ratetechnique. For example, the processing device 140B may update thepreliminary scatter sinogram generator based on the training samples byperforming one or more third iterations. During the third iterations,the value of the learning rate may cycle one or more times. In eachcycle, the learning rate may increase from the lowest learning rate tothe highest learning rate, and then decrease from the highest learningrate back to the lowest learning rate. The count of the cycle(s) may beany positive integer, which may be set manually by a user or determinedby the processing device 140B according to, for example, the count ofthird iterations. Using the cycle learning rate technique, theparameters of the preliminary scatter sinogram generator may beoptimized in a wider value range, thereby making the trained scattersinogram generator have an improved generalization ability.

In some embodiments, as aforementioned, the training of the preliminarymodel may include one or more second iterations, and training of thepreliminary scatter sinogram generator may include one or more thirditerations. A second iteration and a third iteration may be performed ina similar manner. For illustration purposes, an exemplary currentiteration of the second iteration(s) in the first training stage isdescribed in the following description. The current iteration may beperformed based on at least a portion of the training samples. In someembodiments, a same set or different sets of training samples may beused in different iterations in training the preliminary model. In thecurrent iteration, for each of at least a portion of the trainingsamples, the processing device 140B may generate a predicted targetscatter sinogram of the corresponding sample subject by inputting thesample preliminary scatter sinogram and the sample true sinogram (e.g.,in the form of a sample concatenated sinogram) into an updatedpreliminary model determined in a previous second iteration, and theupdated preliminary model may output a predicted target scattersinogram. The processing device 140B may then determine a value of aloss function based on the predicted target scatter sinogram and thesample target scatter sinogram (or the cropped sample target scattersinogram) of each of the at least a portion of the training samples. Theloss function may be used to evaluate the accuracy and reliability ofthe updated preliminary model, for example, the smaller the lossfunction is, the more reliable the updated preliminary model is.Exemplary loss functions may include an L1 loss function, a focal lossfunction, a log loss function, a cross-entropy loss function, a Diceloss function, etc. The processing device 1406 may further update thevalue(s) of the model parameter(s) of the updated preliminary model tobe used in a next iteration based on the value of the loss functionaccording to, for example, a backpropagation algorithm.

In some embodiments, the one or more second iterations may be terminatedif a termination condition is satisfied in the current iteration. Anexemplary termination condition may be that the value of the lossfunction obtained in the current iteration is less than a predeterminedthreshold. Other exemplary termination conditions may include that acertain count of second iterations is performed, that the loss functionconverges such that the differences of the values of the loss functionobtained in consecutive second iterations are within a threshold, etc.If the termination condition is satisfied in the current iteration, theprocessing device 1406 may designate the updated preliminary model asthe preliminary scatter sinogram generator.

It should be noted that the above description regarding the process 800is merely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations or modifications may be madeunder the teachings of the present disclosure. However, those variationsand modifications do not depart from the scope of the presentdisclosure. In some embodiments, one or more operations may be added oromitted. For example, after the scatter sinogram generator is generated,the processing device 1406 may further test the scatter sinogramgenerator using a set of testing samples. Additionally or alternatively,the processing device 1406 may update the scatter sinogram generatorperiodically or irregularly based on one or more newly-generatedtraining samples (e.g., new sample scatter sinogram generated in medicaldiagnosis).

In some embodiments, the preliminary model may be trained with a singletraining stage or more than two training stages. If multiple trainingstages are implemented, a same gradient descent algorithm or differentgradient descent algorithms may be utilized in different trainingstages. Additionally or alternatively, a same learning rate schedule ordifferent learning rate schedules may be utilized in different trainingstages.

FIG. 9 is a schematic diagram illustrating an exemplary training stageand prediction stage of a scatter sinogram generator 970 according tosome embodiments of the present disclosure.

In the training stage, a plurality of training samples, each of whichincludes a sample preliminary scatter sinogram, a sample true sinogram,and a sample target scatter sinogram, may be used to train a preliminarymodel 920. As shown in FIG. 9, an input 910 may be inputted into thepreliminary model 920, and the preliminary model 920 may output apredicted target scatter sinogram 930 in response to the input 910. Theinput 910 may be generated based on a sample preliminary scattersinogram and a sample true sinogram of each training sample. Forexample, a sample concatenated sinogram may be generated based on thesample preliminary scatter sinogram and the sample true sinogram of eachtraining sample as the input 910. For each training sample, thepredicted target scatter sinogram 930 may be compared with the sampletarget scatter sinogram 940 of the training sample to determine a valueof a loss function. The preliminary model 920 may then be updated togenerate the scatter sinogram generator 970 based on the value of theloss function.

In the prediction stage, PET data 950 of a subject may be acquired in aPET scan of the subject. An input 960 including a preliminary scattersinogram and a true sinogram may be generated based on the PET data 950.The scatter sinogram generator 970 may be used to receive an input 960and output a target scatter sinogram 980. The target scatter sinogram980 may have a higher image quality (e.g., a lower noise level) than thepreliminary scatter sinogram and be used for reconstructing a target PETimage of the subject.

FIG. 10 is a schematic diagram illustrating an exemplary preliminarymodel 1000 according to some embodiments of the present disclosure. Insome embodiments, the preliminary model 1000 may be trained to generatea scatter sinogram generator as described elsewhere in this disclosure(e.g., FIG. 5 and the relevant descriptions).

As shown in FIG. 10, the preliminary model 1000 may be configured togenerate an output 1020 by receiving and processing an input 1010. Forexample, the input 1010 may include a training sample as in connectionwith FIG. 8. The training sample may include a sample preliminaryscatter sinogram and a sample target scatter sinogram relating toscattering events detected in a sample PET scan of a sample subject. Theoutput 1020 may include a predicted target scatter sinogram of thetraining sample.

The preliminary model 1000 may include an input layer, an output layer,and a plurality of sequentially connected layers 1001, 1002, 1003, 1004,and 1005. The layer 1001 may include two convolution units (representedby a circle marked with A) and a residual block (represented by a circlemarked with B). Each of the two convolution units (or referred to asConv-BN-ReLU units) may include a convolution block, a batchnormalization block, and a rectified linear unit function block. In aconvolution unit, the convolution block may be configured to extractfeature information (e.g., a feature map or a feature vector) from aninput, the batch normalization (BN) block may be configured to normalizean output of the convolution block, and the rectified linear unit (ReLU)function block may be configured to perform a nonlinear transformationon an input received from the batch normalization block.

Each of the layers 1002, 1003, and 1004 may include a first residualblock and a second residual block. In some embodiments, each of thefirst and second residual blocks may have a same configuration as or asimilar configuration to a residual block 1030 as shown in FIG. 10. Theresidual block 1030 may include a first layer 1031 and a second layer1032. The first layer 1031 may include two Conv-BN-ReLU units, and thesecond layer 1032 may include one Conv-BN-ReLU unit. Each of the firstlayer 1031 and the second layer 1032 may process an input (e.g., anoutput of a previous layer) and generate an output (e.g., a feature mapor a feature vector). The outputs of the first layer 1031 and the secondlayer 1032 may be added together to generate an output 1033 of theresidual block 1030. Compared with a conventional U-net model thatmainly uses convolution blocks for feature extraction, the U-shapedpreliminary model 1000 disclosed herein may achieve an improved accuracyand training efficiency by using the residual blocks. For example, intraining the preliminary model 1000, the residual blocks may betterpreserve information or features, avoid the problem of vanishinggradient, and facilitate model convergence. In some embodiments, each ofthe layers 1002, 1003, and 1004 may be referred to as a residual layer.

As shown in FIG. 10, the convolution units of the layer 1001 and thefirst residual blocks of the layers 1002, 1003 and 1004 may be connectedto a next layer via a downsampling path along which information (e.g.,an output of a specific block) may be downsampled. For example, thedownsampling path may include a max-pooling layer and a dropblock layer.The max-pooling layer may be configured to perform a max-poolingoperation on an output (e.g., a feature map or a feature vector) of theprevious layer, so as to downsample the output of the previous layer.The dropblock layer may be a regularization algorithm that dropscontiguous regions from a feature map, which may prevent the preliminarymodel 1000 from overfitting and make the generated scatter sinogramgenerator have an improved reliability and/or an improved generalizationability.

The residual block of the layer 1005 and the second residual block ofeach of the layers 1002 to 1004 may be connected to the previous layervia an upsampling path along which information (e.g., an output of aspecific block) may be upsampled. For example, the upsampling path mayinclude a transpose convolution layer configured to upsample an outputof a previous layer. Using the downsampling path and the upsamplingpath, the preliminary model 1000 may perform feature extraction atdifferent scales (or image resolutions) and have a higher modelaccuracy.

In some embodiments, the preliminary model 1000 may further include aplurality of skip connections (represented by dotted arrow lines in FIG.10). A skip connection may connect two blocks of a same layer ordifferent layers and be configured to combine information of theconnected blocks. For example, a skip connection may establish aconnection between the first and second residual blocks of the layer1002. The skip connection may be used to transmit an output of the firstresidual block to the second residual block, such that original featureinformation extracted by the first residual block may be preserved tofacilitate image information recovery and improve the accuracy of theresulting scatter sinogram generator.

Additionally or alternatively, the residual block of the layer 1001 maybe connected to a convolution block represented by a circle marked withC. The preliminary model 1000 may further include a long residualconnection that directly connects the input layer and the convolutionblock C. For example, using the long residual connection, thepreliminary model 1000 may learn a difference between the samplepreliminary scatter sinogram of a training sample and a predicted targetscatter sinogram outputted by the residual block of the layer 1001,thereby facilitating or expediting the convergence of the preliminarymodel 1000.

In some embodiments, the preliminary model 1000 may be a narrow deeplearning model, that is, the count of feature maps extracted from theinput 1010 by the preliminary model 1000 may be smaller than athreshold. In addition, 1×1 convolution filters may be used in thepreliminary model 1000. In this way, the resulting scatter sinogramgenerator may have a higher reconstruction efficiency in application(e.g., be capable of generating a target scatter sinogram quickly in aperiod shorter than a threshold period).

It should be noted that the example in FIG. 10 is merely provided forthe purposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. For example, thepreliminary model 1000 may include any number of layers. Each layer mayinclude any count and/or type of blocks. As another example, thepreliminary model 1000 may include one or more additional componentsand/or one or more components (e.g., one or more of the skip connectionsand/or the long residual connection) illustrated in FIG. 10 may beomitted. As yet another example, the preliminary model 1000 may includeany count of residual blocks.

Examples

The following examples are provided for illustration purposes and notintended to be limiting.

FIG. 11 is a schematic diagram illustrating exemplary sinograms of afirst subject generated using different techniques based on PET data ofthe first subject according to some embodiments of the presentdisclosure. As shown in FIG. 11, an image 1100A is a preliminary scattersinogram of the first subject, which was generated based on a firstcount of coincidence events of the PET data using a Monte-Carlosimulation algorithm. An image 11006 is a true sinogram of the firstsubject. An image 1100C is a target scatter sinogram of the firstsubject, which was generated by applying a scatter sinogram generator asdescribed in connection with operation 503. An image 1100D is a scattersinogram of the first subject, which was generated based on a secondcount of coincidence events of PET data using the Monte-Carlo simulationalgorithm. The second count is higher than the first count.

As shown in FIG. 11, the image 1100C is smoother and includes morescattering event information than the image 1100A, which suggests thatthe image 1100C has a higher image quality (e.g., a lower noise level)than the image 1100A and may be more suitable for reconstructing atarget PET image of the first subject. In addition, the image quality ofthe image 1100C is close to that of the image 1100D. Compared to theimage 1100C, the generation of the image 1100D simulated from a greatercount of coincidence events costs more time. For example, the timeneeded for generating the image 1100D was in the order of 100 times thatneeded for generating the image 1100A.

FIG. 12 is a schematic diagram illustrating exemplary sinograms of asecond subject generated using different techniques based on PET data ofthe second subject according to some embodiments of the presentdisclosure. As shown in FIG. 12, an image 1200A is a preliminary scattersinogram of the second subject, which was generated based on a firstcount of coincidence events of the PET data using a Monte-Carlosimulation algorithm. An image 1200B is a true sinogram of the secondsubject. An image 1200C is a target scatter sinogram of the secondsubject, which was generated by applying a scatter sinogram generator asdescribed in connection with operation 503. An image 1200D is a scattersinogram of the second subject, which was generated based on a secondcount of coincidence events of the PET data using the Monte-Carlosimulation algorithm. The second count is higher than the first count.

As shown in FIG. 12, the image 1200C is smoother and includes morescattering event information than the image 1200A, which suggests thatthe image 1200C has a higher image quality (e.g., a lower noise level)than the image 1200A and may be more suitable for reconstructing atarget PET image of the second subject. In addition, the image qualityof the image 1200C is close to that of the image 1200D. Compared to theimage 1200C, the generation of the image 1200D simulated from a greatercount of coincidence events costs more time.

Accordingly, the systems and methods disclosed herein may be used togenerate a target scatter sinogram with an improved image quality (e.g.,a reduced noise level) using less time and fewer computation recourses,which in turn, may facilitate the reconstruction of a target PET imageof the subject having a desired image quality (e.g., with less scatternoises).

It will be apparent to those skilled in the art that various changes andmodifications can be made in the present disclosure without departingfrom the spirit and scope of the disclosure. In this manner, the presentdisclosure may be intended to include such modifications and variationsif the modifications and variations of the present disclosure are withinthe scope of the appended claims and the equivalents thereof.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure, and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “module,” “unit,” “component,” “device,” or “system.”Furthermore, aspects of the present disclosure may take the form of acomputer program product embodied in one or more computer readable mediahaving computer readable program code embodied thereon.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including electro-magnetic, optical, or thelike, or any suitable combination thereof. A computer readable signalmedium may be any computer readable medium that is not a computerreadable storage medium and that may communicate, propagate, ortransport a program for use by or in connection with an instructionexecution system, apparatus, or device. Program code embodied on acomputer readable signal medium may be transmitted using any appropriatemedium, including wireless, wireline, optical fiber cable, RF, or thelike, or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB. NET,Python or the like, conventional procedural programming languages, suchas the “C” programming language, Visual Basic, Fortran 2003, Perl, COBOL2002, PHP, ABAP, dynamic programming languages such as Python, Ruby andGroovy, or other programming languages. The program code may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider) or in a cloud computing environment or offered as aservice such as a Software as a Service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose, and that the appendedclaims are not limited to the disclosed embodiments, but, on thecontrary, are intended to cover modifications and equivalentarrangements that are within the spirit and scope of the disclosedembodiments. For example, although the implementation of variouscomponents described above may be embodied in a hardware device, it mayalso be implemented as a software only solution, e.g., an installationon an existing server or mobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various embodiments. This method ofdisclosure, however, is not to be interpreted as reflecting an intentionthat the claimed subject matter requires more features than areexpressly recited in each claim. Rather, claim subject matter lie inless than all features of a single foregoing disclosed embodiment.

In some embodiments, the numbers expressing quantities or propertiesused to describe and claim certain embodiments of the application are tobe understood as being modified in some instances by the term “about,”“approximate,” or “substantially.” For example, “about,” “approximate,”or “substantially” may indicate a certain variation (e.g., ±1%, ±5%,±10%, or ±20%) of the value it describes, unless otherwise stated.Accordingly, in some embodiments, the numerical parameters set forth inthe written description and attached claims are approximations that mayvary depending upon the desired properties sought to be obtained by aparticular embodiment. In some embodiments, the numerical parametersshould be construed in light of the number of reported significantdigits and by applying ordinary rounding techniques. Notwithstandingthat the numerical ranges and parameters setting forth the broad scopeof some embodiments of the application are approximations, the numericalvalues set forth in the specific examples are reported as precisely aspracticable. In some embodiments, a classification condition used inclassification is provided for illustration purposes and modifiedaccording to different situations. For example, a classificationcondition that “a probability value is greater than the threshold value”may further include or exclude a condition that “the probability valueis equal to the threshold value”.

1. A system for positron emission tomography (PET) image reconstruction,comprising: at least one storage device including a set of instructions;and at least one processor configured to communicate with the at leastone storage device, wherein when executing the set of instructions, theat least one processor is configured to direct the system to performoperations including: obtaining PET data of a subject, the PET databeing associated with a plurality of coincidence events, the pluralityof coincidence events including scattering events; generating, based onthe PET data, a preliminary scatter sinogram relating to the scatteringevents; generating, based on the preliminary scatter sinogram, a targetscatter sinogram relating to the scattering events by applying a scattersinogram generator, the target scatter sinogram having a higher imagequality than the preliminary scatter sinogram; and reconstructing, basedon the PET data and the target scatter sinogram, a target PET image ofthe subject.
 2. The system of claim 1, wherein the generating apreliminary scatter sinogram relating to the scattering eventscomprises: generating, based on the PET data, a preliminary PET image ofthe subject; and generating, based on the preliminary PET image, thepreliminary scatter sinogram, wherein the target PET image includes lessscattering noises than the preliminary PET image.
 3. The system of claim2, wherein the generating the preliminary scatter sinogram based on thepreliminary PET image comprises: obtaining an attenuation map of thesubject; and generating, based on the attenuation map and thepreliminary PET image, the preliminary scatter sinogram.
 4. The systemof claim 3, wherein the preliminary scatter sinogram is generated basedon the attenuation map and the preliminary PET image according to aMonte-Carlo simulation algorithm.
 5. The system of claim 2, wherein theplurality of coincidence events further include true coincidence events,and the generating a preliminary PET image of the subject based on thePET data comprises: generating, based on PET data, a prompt sinogram anda delay sinogram; determining, based on the prompt sinogram and thedelay sinogram, a true sinogram relating the true coincidence events andthe scattering events; and generating, based on the true sinogram, thepreliminary PET image of the subject.
 6. The system of claim 5, whereinthe generating a target scatter sinogram relating to the scatteringevents by applying a scatter sinogram generator comprises: generatingthe target scatter sinogram by processing the preliminary scattersinogram and the true sinogram using the scatter sinogram generator. 7.The system of claim 6, wherein the generating the target scattersinogram by processing the preliminary scatter sinogram and the truesinogram using the scatter sinogram generator comprises: normalizing thepreliminary scatter sinogram and the true sinogram; generating aconcatenated sinogram by concatenating the normalized preliminaryscatter sinogram and the normalized true sinogram; and generating thetarget scatter sinogram by processing the concatenated sinogram usingthe scatter sinogram generator.
 8. The system of claim 1, wherein thereconstructing a target PET image of the subject based on the PET dataand the target scatter sinogram comprises: generating, based on thetarget scatter sinogram and the PET data, an updated PET image of thesubject; generating a final scatter correction sinogram based on thetarget scatter sinogram and the updated PET image; and reconstructing,based on the PET data and the final scatter correction sinogram, thetarget PET image of the subject.
 9. The system of claim 8, wherein thegenerating an updated PET image of the subject based on the targetscatter sinogram and the PET data includes: generating a scattercorrection sinogram by correcting the target scatter sinogram; andgenerating, based on the scatter correction sinogram and the PET data,an updated PET image of the subject, and wherein the generating a finalscatter correction sinogram based on the target scatter sinogram and theupdated PET image comprises generating the final scatter correctionsinogram by iteratively updating the scatter correction sinogram basedon the updated PET image.
 10. The system of claim 1, wherein the scattersinogram generator comprises a plurality of sequentially connectedlayers, and the plurality of sequentially connected layers comprises: aplurality of residual layers, at least one of the plurality of residuallayers including a first residual block and a second residual block, thefirst residual block being connected to a next layer via a downsamplingpath, and the second residual block being connected to a previous layervia an upsampling path.
 11. The system of claim 1, wherein the scattersinogram generator is trained according to a model training processincluding: obtaining a plurality of training samples each of whichincludes a sample preliminary scatter sinogram and a sample targetscatter sinogram relating to sample scattering events detected in asample PET scan of a sample subject, the sample target scatter sinogramhaving a higher image quality than the sample preliminary scattersinogram; and generating the scatter sinogram generator by training apreliminary model using the plurality of training samples.
 12. Thesystem of claim 1, wherein the image quality of a certain scattersinogram relates to at least one of a noise level, a contrast ratio, ora smoothness of the certain scatter sinogram, the certain scattersinogram being the target scatter sinogram or the preliminary scattersinogram.
 13. A system, comprising: at least one storage device storinga set of instructions for generating a scatter sinogram generator; andat least one processor configured to communicate with the at least onestorage device, wherein when executing the set of instructions, the atleast one processor is configured to direct the system to performoperations including: obtaining a plurality of training samples each ofwhich includes a sample preliminary scatter sinogram and a sample targetscatter sinogram relating to sample scattering events detected in asample PET scan of a sample subject, the sample target scatter sinogramhaving a higher quality than the sample preliminary scatter sinogram;and generating the scatter sinogram generator by training a preliminarymodel using the plurality of training samples.
 14. The system of claim13, wherein each of the plurality of training samples further comprisesa sample true sinogram relating to sample true coincidence events andthe sample scattering events detected in the corresponding sample PETscan.
 15. The system of claim 14, wherein the generating the scattersinogram generator by training a preliminary model using the pluralityof training samples comprises: for each of the plurality of trainingsamples, normalizing the sample preliminary scatter sinogram, the sampletarget scatter sinogram, and the sample true sinogram of the trainingsample; generating a cropped sample preliminary scatter sinogram, acropped sample target scatter sinogram, a cropped sample true sinogramby cropping the normalized sample preliminary scatter sinogram, thenormalized sample target scatter sinogram, and the normalized sampletrue sinogram of the training sample, respectively; and generating asample concatenated sinogram by concatenating the cropped samplepreliminary scatter sinogram and the cropped sample true sinogram; andgenerating the scatter sinogram generator by training the preliminarymodel using the sample concatenated sinogram and the cropped sampletarget scatter sinogram of each of the plurality of training samples.16. The system of claim 14, wherein the generating the scatter sinogramgenerator by training a preliminary model using the plurality oftraining samples comprises: generating a preliminary scatter sinogramgenerator by training the preliminary model using the plurality oftraining samples according to a first gradient descent algorithm; andgenerating the scatter sinogram generator by training the preliminaryscatter sinogram generator using the plurality of training samplesaccording to a second gradient descent algorithm, wherein the secondgradient descent algorithm is different from the first gradient descentalgorithm.
 17. The system of claim 16, wherein the first gradientdescent algorithm is an Adam optimization algorithm, and the secondgradient descent algorithm is a stochastic gradient descent(SGD)+Momentum optimization algorithm.
 18. The system of claim 16,wherein the generating the scatter sinogram generator by training apreliminary model using the plurality of training samples furthercomprises: determining a first learning rate with respect to the firstgradient descent algorithm according to a learning rate range testtechnique; and determining a second learning rate with respect to thesecond gradient descent algorithm according to a cycle learning ratetechnique, wherein the generating a preliminary scatter sinogramgenerator by training the preliminary model using the plurality oftraining samples according to a first gradient descent algorithmcomprises generating the preliminary scatter sinogram generator bytraining the preliminary model using the plurality of training samplesaccording to the first gradient descent algorithm and the first learningrate; and the generating the scatter sinogram generator by training thepreliminary scatter sinogram generator using the plurality of trainingsamples according to a second gradient descent algorithm comprisesgenerating the scatter sinogram generator by training the preliminaryscatter sinogram generator using the plurality of training samplesaccording to the second gradient descent algorithm and the secondlearning rate.
 19. The system of claim 13, wherein the scatter sinogramgenerator comprises a plurality of sequentially connected layers, andthe plurality of sequentially connected layers comprises: a plurality ofresidual layers, at least one of the plurality of residual layersincluding a first residual block and a second residual block, the firstresidual block being connected to a next layer via a downsampling path,and the second residual block being connected to a previous layer via anupsampling path.
 20. A method for positron emission tomography (PET)image reconstruction, comprising: obtaining PET data of a subject, thePET data being associated with a plurality of coincidence events, theplurality of coincidence events including scattering events; generating,based on the PET data, a preliminary scatter sinogram relating to thescattering events; generating, based on the preliminary scattersinogram, a target scatter sinogram relating to the scattering events byapplying a scatter sinogram generator, the target scatter sinogramhaving a higher image quality than the preliminary scatter sinogram; andreconstructing, based on the PET data and the target scatter sinogram, atarget PET image of the subject. 21-40. (canceled)